This letter addresses the joint estimation of frequency and direction-of-arrival for multiple uncorrelated narrowband sources within a wide bandwidth under sub-Nyquist sampling. We propose an architecture employing a sparse co-array where each element is followed by two parallel channels: one delay-free and the other with a fixed, identical delay, requiring no optimization. For this space-time structure, we develop a joint estimation algorithm based on covariance matrix extension, which increases the degrees of freedom, resolves more sources than physical sensors, and automatically pairs the estimated carrier frequencies with their corresponding directions of arrival. Simulation results demonstrate that the proposed method achieves higher estimation accuracy.

This work prioritizes the suppression of mainlobe deceptive jamming within coarray Frequency Diverse Nested Array (FDNA) Multiple-Input Multiple-Output (MIMO) radar architectures. To overcome the range resolution limitation in the conventional FDA, a novel FDNA structure is proposed. Leveraging differential processing for virtual aperture extension, the design enables precise discrimination of mainlobe deceptive jamming and target. Subsequently, a Spatial Smoothing-based Minimum Variance Distortionless Response (SS-MVDR) beamformer is introduced to eliminate the contamination of training samples by target data during jamming suppression. Furthermore, a frequency offset selection strategy is developed to simultaneously suppress both rapid and delayed repeated jamming. The efficacy of the proposed scheme in suppressing mainlobe deceptive jamming is confirmed by simulation results.

This paper investigates transceiver design optimization strategies for frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar-communication coexistence (RCC) systems, focusing on both radar-centric and communication-centric modes. Specifically, the former formulates the design problem to maximize the signal-to-interference-plus-noise ratio (SINR) in mainlobe deceptive jammer scenarios, whereas the latter aims to maximize the communication rate while simultaneously satisfying a predefined radar SINR constraint. In this framework, practical constraints pertaining to the radar’s transmitted waveform, communication codebook, frequency increment, and receive filter are taken into account. To address the resultant non-convex and NP-hard optimization problems, a maximum block improvement (MBI) approach is employed, where the variables are alternately examined, which are achieved either by leveraging closed-form expressions and hidden convexities or by resorting to the minorization-maximization (MM) approach, while keeping the remaining parameters fixed. The convergence performance of the devised algorithm is thoroughly examined, alongside their computational complexity analyses. Numerical results are provided to validate the efficacy of our approach against mainlobe deceptive jammers, demonstrating superior SINR and communication rate performance compared to existing optimization strategies and benchmark system frameworks.

This work addresses the challenges of communication signal detection and direction of arrival (DOA) estimation in integrated sensing and communications (ISAC) systems with hardware imperfections. Conventional signal processing techniques often fail to effectively manage the complex nonlinearities caused by hardware imperfections, such as those introduced by power amplifiers and local oscillators. Recently, deep neural networks (DNNs) have been employed to mitigate the hardware imperfections, which however require a substantial amount of pilot signals for training, leading to unacceptable overhead and impracticality in fast time-varying channels. In this work, we employ an NN to characterize the nonlinear system, and propose a novel iterative approach to joint NN-based nonlinear system model learning, signal detection and DOA estimation. Instead of relying on pilot signals for NN learning, the proposed approach utilizes communication data signals as virtual training samples, enabling more accurate nonlinear model learning, which subsequently enhances signal detection and DOA estimation. A Bayesian framework is applied to the joint problem, wherein the NN parameters, the communication signals and the DOAs are jointly obtained by developing a message passing based inference algorithm. In particular, we impose sparse priors on the weights of the NN, so that overfitting can be better handled, resulting in significant improvement in system modeling performance. Extensive simulation results show that, compared to the state-of-the-art approaches, the proposed one delivers significantly better performance.

Polarimetric multiple-input multiple-output (MIMO) radar can mitigate polarization mismatch and achieve higher-dimensional target state sensing. Meanwhile, sparse array configurations offer increased degrees of freedom, reduce mutual coupling effects, and lower implementation costs. In this paper, we propose a joint direction-of-departure (DOD), direction-of-arrival (DOA), and polarization estimation method for sparse polarimetric MIMO radar. To fully exploit the multi-dimensional structure of the received data, we adopt a tensor-based framework. Specifically, the covariance tensor of the sparse polarimetric MIMO radar is transformed into that of a virtually uniform polarimetric MIMO radar, and spatial tensor partitioning is further introduced to increase the number of identifiable targets. The CANDECOMP/PARAFAC decomposition is subsequently employed to estimate the joint spatial and polarimetric parameter in a unified framework. Simulation results demonstrate the effectiveness and superiority of the proposed method.

This work investigates communication signal detection and direction of arrival (DOA) estimation for an integrated sensing and communications (ISAC) system with multiple hardware imperfections, including power amplifier nonlinearity, in-phase and quadrature phase imbalance, and phase-gain error (PGE). Conventional signal processing techniques struggle with the complex nonlinearities arising from these imperfections. Recently, deep neural networks (DNNs) have been employed to mitigate hardware impairments; however, they require a substantial number of pilot signals for training, leading to significant overhead, making them impractical in many applications. In this work, we design a signal flow inspired neural network (NN) to characterize the nonlinear ISAC system. Then, we propose a Bayesian method to jointly estimate the parameters of the PGE, the communication signals, and the DOAs by developing a message-passing inference algorithm based on the NN. Extensive simulation results demonstrate that the proposed method provides efficient and robust signal detection and DOA estimation performance under PGE, and significantly outperforms state-of-the-art ones.

Cloud storage has become prevalent in Internet of Things (IoT) systems, attributed to its robust storage capabilities and user convenience. However, the cloud-based storage model, which separates data ownership from management, introduces integrity challenges due to the vulnerability of data to tampering. To address the risk of data loss and ensure recoverability, the implementation of multiple replicas is a common strategy. Nonetheless, traditional multireplica auditing schemes are not well suited for IoT environments that employ lightweight devices with limited computational capabilities. In response to the aforementioned challenges, we propose a novel multireplica auditing scheme named CCMR, aimed at alleviating the heavy computation cost on the device side. Our scheme leverages an efficient aggregated multisignature algorithm, offloading computationally intensive tasks associated with data tags from lightweight devices to cloud service providers (CSPs) equipped with advanced computational power. The innovative cross-cloud multireplica hash tree structure, named CC-MHT, facilitates the secure and efficient verification of data block structures and ensures consistency of replicas across multicloud environments. Furthermore, by utilizing blockchain as a public random source, a robust challenge-response protocol is established to guard against potential audit failures that could arise from collusion between the third-party auditor and CSPs. The experimental results indicate the high efficiency of the proposed scheme in terms of computation cost.

The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. To address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. To the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method.

This letter proposes a time-diverse wide-swath imaging radar transmit waveform optimization design method. First, based on the imaging geometry and zebra maps, we obtained the angles corresponding to the range occlusion zone. Then, using the mapping characteristics between the range frequency and beam scanning angle in time-diverse array (TDA) radar, as well as the occlusion region information, we performed a 2-D optimization design of the transmit waveform in the fast time and range frequency domain. Finally, the limited energy can be effectively skipped over the occlusion regions and flexibly allocated to the observable areas. Compared with the traditional TDA system, this method achieves a larger imaging swath and energy utilization efficiency. The effectiveness of the proposed method is verified through simulation experiments.

This paper explores the mitigation of mainlobe deceptive jammers using a phase-coded planar array. At the modelling stage, a Two-Dimensional (2D) phase coding scheme is employed in the transmit planar array, along with the transmission of Linear Frequency Modulated (LFM) waveform in a planar array. Such a phase-coded planar array not only preserves the waveform properties, but also provides enhanced controllable beamforming capabilities in elevation-azimuth-range 3D domain. In the receiver, by separating and compensating the transmitted signals, the mainlobe deceptive jammers occurring in different range ambiguous regions, are discriminated and suppressed. At the analysis stage, the Capon spectra and spatial filtering results are provided to verify the effectiveness of mainlobe jammer mitigation with the proposed phase-coded planar array.

This letter presents an optimization approach for frequency increments tailored to Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar for target localization. We start to formulate the problem as minimizing the Cramér-Rao Bounds (CRBs) for both range and angle estimation, subject to practical constraints on the frequency increments. To facilitate optimization, the objective function is mathematically transformed, which results in a maximization problem, leveraging its inherent non-negativity of both the numerator and denominator. To address the resultant non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is devised by partitioning the frequency increment vector into distinct blocks, allowing for alternating maximization. In particular, each frequency increment is refined with the MM algorithm, while holding the others fixed, and only the block yielding the maximum objective increment is updated within each iteration. Simulation results are provided to demonstrate the excellent target localization of our proposed approach.

This paper deals with the simultaneous adaptive target detection and parameter estimation utilizing a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar. At the design stage, a linearized model of the received signal is derived, leveraging the centro-Hermitian (persymmetric) structure of the array manifold and considering potential steering vector mismatches related to the actual target parameters. Exploiting this model, the detection problem is formulated and tackled leveraging adaptive detection approaches, resorting to the Persymmetric Generalized Likelihood Ratio (PGLR) and Persymmetric Adaptive Matched Filter (PAMF), respectively. However, they demand the Maximum Likelihood (ML) estimates of target incremental range (i.e., the deviation of the target actual position from the center of the range bin) and angle. Two iterative methods are devised to solve the ML estimation problem. The former is based on Dinkelbach's algorithm, which is capable of achieving the global optimum, whereas the latter is a fast-converging alternative approach relying on the Coordinate Descent (CD) framework. Numerical results highlight the effectiveness of the proposed simultaneous detection and estimation strategies, also in comparison with suitable benchmarks and counterparts.

Employing a single moving receiver for emitter localization offers numerous advantages, including low cost, ease of implementation, and elimination of clock synchronization. A novel localization method is proposed for a stationary emitter with an unknown Signal Repetition Interval (SRI). We first construct a localization model based on time-of-arrival, taking into account potential missed detections, and then derive the Cramér-Rao Lower Bound. Given that the established model is non-convex, we reformulate the problem into a convex form and convert it to Second-order Cone Programming (SOCP) format. However, the SOCP formulation encounters convex hull issues, which may hinder far-field localization. To address this challenge, we introduce a penalty term to alleviate the convex hull problem for the first time. Simulation results demonstrate the effectiveness of the proposed approach in comparison to existing methods, and the validity of the penalty term is also confirmed.

Correcting the range migration of moving targets and compensating for the coupled phase errors are crucial for ground moving target imaging (GMTIm). Most methods achieve target focusing through parameter search operations with substantial computational complexity. In addition, the inability to address energy spreading caused by the higher order motion further limits the applicability of these methods. To overcome these issues, this article proposes a novel refocusing method without motion parameter estimation for arbitrarily moving ground targets. First, a range frequency difference (RFD) function without parameter estimation is constructed in the range frequency domain. Then, by conjugate multiplication with the RFD function, the coupling between range frequency and slow time of target can been removed. With an appropriate frequency interval selected, the target can also be azimuthally focused. In addition, some practical factors in applications are analyzed in detail. Compared with the traditional methods, the proposed method effectively addresses range migration and azimuth defocusing caused by higher order phase errors. Also, it achieves target focusing without search operations, despite the existence of Doppler center ambiguity and Doppler spectrum splitting. In addition, since it only requires fast Fourier transform (FFT), inverse FFT (IFFT), and matrix multiplication operations, this method achieves high computational efficiency. The efficacy of the proposed method is confirmed through the examination of both simulated and real data.

Spaceborne distributed multi-transmit multi-receive (SD-MIMO) radar has great potential to enhance the capability of ground moving target indication (GMTI), particularly with the increasing of space-time-frequency degrees of freedom. However, GMTI performance is severely limited by complex geographical clutter. To address this issue, we proposed a clutter suppression method assisted by online classification using the range-Doppler-band 3D dataset of SD-MIMO radar. The core points and innovations of our method are summarized as follows. Firstly, we extend Markov random field (MRF) classification to 3D space by integrating a 3D adaptive weighted penalty (3D-AWP) function and a probability distribution model of multi-channel clutter. The 3D-AWP function is constructed using a novel spatial distance metric designed to characterize the interaction potential of image pixels within their 3D neighborhood. This ensures that the MRF smooth factor can be adaptively adjusted in both homogeneous and inhomogeneous clutter regions through different weighting values. The probability distribution model of clutter is established by the K-Wishart distribution, which incorporates amplitude and interferometric phase between multiple spatial channels. Then, under the Bayesian framework, we achieve classification of multiband SAR images by solving the maximum posterior probability problem. Subsequently, the homogeneous and inhomogeneous regions are identified based on classification results. And adaptive clutter suppression processing is performed independently for each sub-region. Finally, the SAR-GMTI experiments are performed utilizing synthetic data and measured data. The results demonstrated an improvement in clutter suppression capability by 2 dB and an enhancement in target oSCNR by 1.5 dB compared to traditional methods.

In recent years, the low-rank matrix recovery theory has acquired widespread application in the radar system. For multichannel synthetic aperture radar systems, the robust principal component analysis (RPCA) has proven to be a valuable technique for effectively distinguishing moving targets from static background clutter within the image domain. However, in nonideal environments, the RPCA is susceptible to channel errors and strong clutter, resulting in degraded target detection performance. To resolve this issue, a slow ground-moving target indication (GMTI) processing algorithm is proposed in this article. First, the sample selection and data reconstruction (DR) are used to further compensate for channel imbalance error and registration error. Next, an RPCA optimization framework is proposed to mitigate the issue of elevated false alarm rates caused by heterogeneous environments, and the sparse matrix is obtained through the application of the alternating direction method of multipliers (ADMM). The proposed optimization model not only avoids excessive punishment of large singular values by kernel norm weighting but also further improves the performance of target detection by introducing a difference matrix and a Fourier matrix. Finally, the estimation of the target’s radial velocity is accomplished through the utilization of the adaptive match filtering (AMF) algorithm. Compared with the traditional RPCA algorithm, the proposed algorithm significantly reduces the false alarm rate under the background of strong clutter. Theoretical analyses and measured data results verify the effectiveness of the proposed algorithm.

Ground Moving Target Indication (GMTI) has always been one of the key tasks in synthetic aperture radar (SAR) systems. For SAR-GMTI, existing GMTI algorithms can be classified into traditional signal processing algorithms and low-rank matrix recovery algorithms. Space-time adaptive processing (STAP), as a typical traditional approach, has shown excellent performance in detecting moving targets in real applications; low-rank matrix recovery algorithms have become one of the recent research hotspots. Robust principal component analysis (RPCA), as a typical low-rank matrix recovery approach, has been applied in SAR-GMTI due to its ability to decompose an approximate low-rank matrix into a low-rank component and a sparse component. However, a drawback of RPCA algorithms is its relatively high false alarm rate caused by the energy leakage from strong clutter. The detection performance of the STAP algorithm may degrade when the training samples are contaminated. To address these challenges, we propose a block-based RPCA-STAP algorithm that integrates traditional adaptive clutter suppression methods into the RPCA framework. The proposed algorithm first implements clutter classification using the Markov random field (MRF) image segmentation algorithm. Then, RPCA-STAP suppression is applied to different clutter blocks, which not only satisfies the IID requirement for STAP training samples but also reduces the false alarm rate caused by strong clutter in the RPCA algorithm by introducing STAP. Additionally, considering the sensitivity of GMTI algorithms to channel errors, we propose an improved adaptive 2-D calibration (IA2DC) algorithm to further enhance channel correlation. Simulation and real data experiments validate the effectiveness of the proposed algorithms.

It is well-known that for a maneuvering target, its inverse synthetic aperture radar (ISAR) imaging quality may significantly deteriorate using the classical range-Doppler (RD) algorithm. To address this issue, this article proposes a novel ISAR imaging algorithm based on the generalized second-order time-scaled transform (GSOTST). In the proposed method, the multicomponent cubic phase signal (CPS) modeling is adopted for the radar echo signal after translational motion compensation (TMC) to portray the phase change characteristics more accurately. First, the target signal is transformed into the slow-time-delay-time domain using a correlation kernel function (CKF). Subsequently, the nonstationary phase is eliminated in the slow-time-generalized delay-time-frequency (GDTF) domain, and the GSOTST is used to decouple the temporal variables. Finally, the generalized Fourier transform is performed to transform the signal into the 2-D frequency domain, where the energy of the target signal is integrated into a well-focused 2-D peak, enabling the high-precision target parameter estimation and finely focused ISAR imaging. The experimental results from both simulation and real-measured data validate the effectiveness of the proposed algorithm.

Ground moving target focusing performance and processing efficiency are two important metrics in the synthetic aperture radar (SAR) system. Owing to the complex nonstationary phases caused by target motions, ground maneuvering targets are commonly smeared and distorted in an SAR image. To realize the ground maneuvering target imaging, exhaustive high-order parameter searching and optimization processing are usually needed to achieve the optimal processing performance. However, the large computational resource consumption may limit their real-time processing capabilities. To deal with this issue, an efficient SAR imaging approach for ground maneuvering targets is proposed by adopting the mixed symmetric function (MSF) transform. After performing the multichannel SAR clutter rejection and target initial detection by applying the constant false alarm rate (CFAR) technique, the linear envelop shift of detected ground target is eliminated by using the well-known Keystone transform (KT). Then, an MSF is constructed to rectify the range curvature and relieve the Doppler broadening after carrying out the nonuniform fast Fourier transform (NUFFT) corresponding to the second-order time variable. After removing the second-order Doppler spreading influence, the quadratic chirp parameter is estimated via the NUFFT after the symmetrical correlation processing. Finally, after accomplishing the motion compensation, the ground maneuvering target can be well focused and relocated in the SAR image. Compared with the traditional approaches, the proposed approach not only realizes the high-precision Doppler parameter estimation, but is also computationally efficient without grid searching procedure. Numerical simulation and real-measured SAR experiment results are depicted to verify the correctness and effectiveness of the proposed approach.

Due to the limited resources available on small synthetic aperture radar (SAR) platforms, such as unmanned aerial vehicles (UAVs), one-bit SAR imaging has emerged as a promising technique, particularly with the rapid development of low-altitude economy. One-bit SAR is capable of leveraging a very simple and cheap one-bit analog-to-digital converter (ADC) to complete the same sensing task as those in the conventional SAR using high-resolution ADC. Nevertheless, the one-bit quantization incurs some intractable problems, such as signal amplitude distortion and the emergence of unwanted interference, which seriously degrade the SAR image quality. To tackle these problems, this work proposes a one-bit SAR imaging strategy that devises a quantization threshold fixed in fast-time but fluctuating in slow-time. Specifically, within the one-bit quantization procedure for each echo pulse, the threshold is fixed, significantly simplifying the SAR system. On the other hand, the slow-time fluctuation of the threshold enables the SAR to reassign spectrum energies, effectively suppressing unwanted interference. The frequency of the threshold and the corresponding pulse repetition frequency (PRF) of the SAR system are elaborately designed. In addition, the fluctuation range of the threshold, which determines the fidelity of SAR images directly, is designed as well, and a closed-form expression for the threshold fluctuation range is determined. The effectiveness of the proposed scheme is demonstrated through the simulated and real-data experiments, confirming that high-quality one-bit SAR images can be achieved.

In ground-based L-band radar sea clutter, Bragg scattering caused by short gravity waves on the sea surface frequently exhibits azimuthal dependence, with higher order Bragg-scattering spectra clearly visible alongside the first-order spectrum. Recent measurements from an L-band airborne moving target detection (MTD) radar, operating in side-looking mode with HH polarization at medium incident angles (30°–60°), near the Zhoushan Fishing Ground in Ningbo, China, also reveal azimuth-dependent and multipeak characteristics, that pose challenges for target detection within the endo-clutter region. To better understand the clutter characteristics in L-band airborne MTD radar, this article investigates the modeling of sea clutter Doppler spectra under medium incident angles ranging from 30° to 60°. Using the small slope approximation (SSA) incorporating a time-varying rough sea surface with spikes, a systemic expression for the Doppler spectrum, accounting for the sea clutter space-time coupling, is derived. Specifically, the Doppler spectrum related to the sea surface is expressed as an azimuth-dependent underlying spectrum weighted by the antenna beam (i.e., the spatial spectrum), while the spectrum due to spikes is represented as a convolution of the spatial spectrum with an azimuth-independent underlying spectrum. Each individual spectrum is characterized with Gaussian profiles, forming the basis of a comprehensive spectrum model that can successfully capture an azimuth-independent peak and approximately 2–7 or more azimuth-dependent peaks in the real-world sea-clutter Doppler spectra. Note that the proposed model is directed against sea echoes with azimuth-dependent scattering properties from the main lobe of the two-way antenna pattern, and thus, suitably characterizes the corresponding sea-clutter Doppler features.

As the electromagnetic spectrum becomes increasingly crowded in recent years, synthetic aperture radar (SAR) is confronted with an escalating amount of radio-frequency interference (RFI). In civilian SAR satellite data, narrowband pulsed RFI (PRFI) is a prevalent interference type that significantly degrades the interpretability of SAR images. Among most approaches for suppressing PRFI, notch filtering methods face significant challenges in threshold selection of signal intensity. Conversely, low-rank and sparse decomposition (LRSD) algorithms, though free of threshold selection, often struggle to satisfy the required low-rank conditions. These limitations underscore the necessity of developing more robust interference suppression methods. In this article, we propose a joint framework of wavelet filtering and fast generalized singular value thresholding-based LRSD (FGSVT-LRSD) method to suppress narrowband PRFI in range–frequency and azimuth–time domain of SAR single-look complex (SLC) data. First, a wavelet domain notch filtering (WNF) method is employed to extract the strong spectral components that are primarily composed of PRFI in the 2-D range spectrum of SAR SLC data, while simultaneously protecting the spectrum of low-energy useful signals. Then, the FGSVT-LRSD method is applied to the extracted strong spectral components to efficiently separate the PRFI from the useful signals. By strategically integrating these two approaches, the proposed framework simultaneously exploits the high-intensity and low-rank properties of PRFI for more precise separation, significantly reduces the sensitivity of threshold selection in the WNF process and facilitates the low-rank conditions required by the FGSVT-LRSD method. Finally, the separated PRFI spectrum is subtracted from the original 2-D range spectrum, resulting in the RFI-suppressed SAR image. Experimental results based on both simulated and measured spaceborne SAR data will be presented to demonstrate that, compared with existing methods, the proposed approach exhibits superior PRFI suppression capabilities and effectively preserves useful signals.

This paper aims to suppress mainlobe deceptive jammers considering the multipath effect in a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. At the problem formulation stage, the overall received signal including the true target, main-lobe deceptive jammers, and burst jamming signal in the presence of multipath propagation, is represented as a "low-rank + low-rank + sparse" decomposition model. Then, an improved Go Decomposition (GoDec) algorithm is developed to recover the components corresponding to the target signal and disturbance (including the mainlobe jammers and burst jamming signal). Furthermore, a data-dependent beamforming approach is implemented to eliminate the mainlobe deceptive jammers, where the covariance matrix is constructed using the recovered disturbance components, and the steering vector is obtained with a priori knowledge of the target position. Numerical results are provided to demonstrate the effectiveness of the devised technique and its superiority over competing methods in suppressing mainlobe deceptive jammers under multipath environments.

Abstract Synthetic aperture radar (SAR) image change detection suffers from poor quality of the difference image and low detection accuracy. Hence, this paper proposes a SAR image change detection method based on a fused difference image and an optimized random forest scheme, termed LRN‐SSARF. Specifically, a fusion operator difference image LRN is proposed, which is generated using a weighted fusion of log‐ratio (LR), ratio (R), and normalized ratio (NoR). This difference image generation method reduces noise's influence. Then, the Otsu algorithm is applied to segment the difference image and select the training samples. The training samples are input into the random forest (RF) model optimised by the sparrow search algorithm (SSA) for training and classification. Finally, the region link is uesd to refine the detection results and generate the final result. The change detection results of six real SAR image scenes highlight that the proposed algorithm has a high detection accuracy, and affords appealing integrity and detailed information about the change regions. Specially, the detection accuracy advantage of the Bangladesh dataset is larger, with the accuracy and Kappa coefficient reaching 98.04% and 92.00%, much higher than the competitor methods.

Distributed aperture coherence multi-transmit multi-receive (MIMO) radar combined with space time adaptive processing (STAP) has great advantage for moving target indication. However, the problems of non-ideal orthogonal waveform and non-stationary clutter are intertwined together, which leads to significant performance degradation of STAP. In this letter, we presented a pragmatic and accurate performance prediction model to interpret the impact mechanism of waveform properties on STAP performance. We deduced a novel clutter covariance matrix (CCM) model with the aforementioned non-ideal factors taking into consideration. On this basis, a closed-form expression of output SCNR loss is derived to predict and evaluate STAP performance. Therefrom the quantitative relationship is established between waveform properties, clutter CCM, and STAP performance. It provides a simple and effective way to predict system performance for radar system design. Finally, numerical simulation results illustrated that the mean prediction error of SCNR loss for proposed model is about 2dB less than conventional approximation model.

This letter proposes a fast Doppler parameter estimation and refocusing method for ground moving targets in a synthetic aperture radar (SAR) system. In the proposed method, after implementing the main-lobe clutter rejection by using the azimuth adaptive processing technique, the range-azimuth positions of smeared target scatterers can be obtained via the constant false alarm rate (CFAR) detection. Then, an autocorrelation function is constructed to transform a moving target signal into the time-frequency plane, where the target parameters can be precisely and efficiently estimated by applying the scaled fast Fourier transform (FFT). Finally, ground moving targets can be well refocused and relocated in a SAR imagery. Compared with the conventional methods, the target output SNR can be enlarged about 3 dB under the low SNR by using the proposed parameter estimation method.

Detecting ships in synthetic aperture radar (SAR) imagery is a pivotal task for marine surveillance and security. Although many deep learning (DL) methods have been proposed for SAR ship detection, they still lack the ability to explore intrinsic scattering features, and their ship target detection capabilities necessitate further enhancements in complex labile environments, especially for small ships. For this reason, this letter proposes a dual branch scattering feature fusion network (SFFNet). First, scattering center feature maps are reconstructed, and then, we design a scattering feature attention fusion module (SFAFM) in view of reconstructed feature maps, which can enhance the prominent feature extraction ability of the network. Moreover, the backbone feature extraction architecture incorporates a dense depthwise block (DDWB) aimed at more effectively fostering information interactions for scattering features and improving the efficiency of the network. To validate the efficacy of the SFFNet, comprehensive experiments were conducted on two public datasets, namely, HRSID and LS-SSDD-v1.0, and experimental results indicated that the detection accuracy reached 98.3%, and the false detection rate decreased to 0.21%. The proposed method can achieve superior performance when benchmarked against other state-of-the-art detection methods.

As the electromagnetic environment increases complexity, electromagnetic signals in the same frequency band will interfere with synthetic aperture radar (SAR), causing distortion in SAR images. Most notch filters set a fixed threshold and zero notch to achieve radio frequency interference (RFI) suppression which is not adaptive when the energy level of the RFI varies. In this letter, an iterative notch filtering method based on range-frequency domain outlier detection is proposed. It uses hybrid indicators to distinguish the differences of the range spectrum of the Z-normalized SAR single-look complex (SLC) image and the zero-mean complex Gaussian distribution, and obtains an adaptive detection threshold based on spectrum data to detect anomalies. Then, 2D notch filters are constructed to achieve RFI mitigation. In addition, the proposed method performs the above operations with adaptive thresholds in an iterative manner by setting termination conditions to suppress remaining RFI. Compared with other methods, the proposed method can offer more precise RFI detection and effective RFI mitigation. Experiments based on simulated and real SAR data demonstrate the efficacy of the proposed method.

Ground moving target detection is one of the foremost tasks for multichannel synthetic aperture radar (SAR) system. The traditional robust principal component analysis (RPCA) method is capable of separating low-rank and sparse components from mixed echo signals, and it has been widely applied in SAR ground moving target indication (GMTI). However, it suffers from sensitivity to channel mismatch, high computational complexity, and excessively high false alarm rates. To address these issues, a novel method that combines adaptive multichannel data reconstruction (DR) with improved pseudo-skeleton decomposition (IPSD) is proposed. First, the iterative weighted approach is presented to precisely reconstruct the multichannel data vector with the joint-pixel model. After that, IPSD is presented to achieve the moving target detection, in which the row and column index sets are selected using the generalized inner product (GIP) and the amplitude histogram distribution criterion. Compared to the existing algorithms, the proposed algorithm effectively addresses the challenge of improving local region coherence in multichannel image sequences. In addition, compared to previous RPCA methods, the proposed algorithm significantly reduces false alarm rates in strong clutter backgrounds while achieving higher efficiency. Simulation results and real SAR data experiments validate the effectiveness of the proposed algorithm.

Using spaceborne transmitter and airborne receiver, the space–air bistatic radar (SABR) system has advantages of wide coverage, antistealth, and anti-interference. However, the SABR encounters severe clutter spreading in spatial–temporal domain since it works in down-looking mode, which would cause serious performance degradation in moving target detection. Moreover, SABR encounters several practical factors, such as the separated transmitter and receiver, the non-side-looking array configuration, wide Earth coverage, and Earth rotation, which would induce range-dependent and high-order nonlinear spatial–temporal-coupled ground/sea clutter. Under this circumstance, this article first investigates the general space-time clutter model of SABR for arbitrary transmitter–receiver geometry and array configuration. Then, the high-order nonlinear coupling relationship of clutter curve is derived, where the expressions of the spatial frequency and Doppler frequency are derived with respect to the eccentric anomaly of bistatic iso-range ring. In the sequel, the spatial–temporal-coupled and range-dependent characteristics of the clutter in SABR is analyzed and discussed in detail for three typical geometrical configurations with high/medium/low elliptical orbit satellite as transmitter and aircraft as receiver. On the basis of the above analysis, an effective clutter compensation approach based on subspace rotation is proposed to eliminate the range dependence of the clutter. Numerical examples of different bistatic geometries are conducted to analyze the spatial–temporal coupling relationship of clutter and validate the effectiveness of the proposed compensation method.

Frequency diverse array (FDA) introduces frequency increment across array elements, resulting in increased controllable degrees-of-freedom (DOFs) in spatial and frequency domains. As different sub-band waveforms are transmitted by different elements simultaneously, it is capable of obtaining a large synthesized bandwidth at the receiver, thus improving the resolution of radar imaging. In this article, a general bandwidth synthesis approach is proposed, which includes flexible shift of each sub-band waveform as well as combination of all these sub-band waveforms in frequency domain. Since the sub-band waveforms can be flexibly shifted in frequency domain, we can obtain an arbitrarily synthesized bandwidth, both wideband and narrowband waveforms. In the proposed approach, the general bandwidth synthesis mechanism is revealed. The analytical expressions of the bandwidth-synthesized signals are derived with an arbitrary shift in frequency domain, resulting in multiresolution synthetic aperture radar (SAR) imaging, which provides the potential for realizing multimode SAR imaging simultaneously. Several specific cases regarding the bandwidth-synthesized signals, including overlapped and non-overlapped, are provided to further explain the bandwidth synthesis approach. The range resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) performance are analyzed. Nonuniform frequency shifts are applied to alleviate the PSLR degradation due to non-overlapped sub-band synthesis. Numerical simulation results of point target and distributed scene target are provided to demonstrate the effectiveness of the proposed approach.

In order to play the key role of automotive millimeter wave radar in intelligent vehicle systems, direction-of-arrival (DOA) estimation is an essential problem to be solved. For practical intelligent driving applications, DOA estimation requires both real-time performance and high accuracy. Due to unique advantages, deep learning (DL) based methods have attracted more attention. Most of the existing DL-based methods require a large number of snapshots, but only a few snapshots can be guaranteed in practical applications. Moreover, they usually model DOA estimation as a multi-label classification task. The output represents the position of signal DOA on the discrete grid, and the resolution will be limited by the grid. In this paper, a new space-time Network (STNet) is proposed, which models DOA estimation as a regression task to achieve the effect of gridless estimation. We design a space correlation extraction module (SCEM) and a time correlation extraction module (TCEM), using the covariance matrix of the received signal and the original received signal as inputs respectively, treat them as different types of data. In these two modules, skip connection dense blocks (SCDBs) and long short-term memory (LSTM) networks are adopted to process two different forms of data. Through such processing, we retain sufficient information, obtain more features for the regression task, and ensure the estimation effect of using a small number of snapshots. The experimental results indicate that the STNet shows obvious performance gain in the case of small snapshots, achieves gridless estimation effect, and demonstrates excellent adaptability in situations where target DOAs are closely positioned.

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

This paper deals with the problem of Doppler ambiguity in a frequency diverse array (FDA) multiple-input and multiple-output (MIMO) radar. In our modeling stage, a spread phase alignment (SPA) method is proposed by utilizing the pulse-dependent transmit spatial frequency. In this respect, the Doppler spread alignment is performed in each transmit pulse based on spatial cancellation. Hence, the point targets, which are de-focused due to the spread Doppler frequency can be focused in the transmit spatial frequency domain. Furthermore, the Doppler ambiguity index is estimated after principal velocity compensation resorting to the maximum likelihood (ML) criterion. Numerical results are provided to verify the effectiveness of the proposed method in resolving the Doppler ambiguity.

The space-based surveillance radar system has a higher field of view and can overcome interference from Earth's atmosphere and terrain occlusion, which has been widely applied in high-threat near-Earth asteroid (NEA) warning and defense applications. Due to the limited power aperture product of the space-based system and the far distance between the radar and asteroid targets, the target signal is extremely weak. Prolonging the coherent accumulation time can effectively improve the radar detection capability of small asteroid targets, but the complex effects of range migration (RM) and Doppler frequency migration (DFM) will degrade the target coherent accumulation performance. To effectively solve this problem, an improved Keystone transform (KT) matched filtering banks method based on particle swarm optimization algorithm is proposed. Compared with traditional methods, the proposed method can not only ensure that the asteroid target detection performance is close to the theoretical optimum, but also reduce the system computation complexity. Simulation results verify the effectiveness of the proposed algorithm.

Due to great success of transformers in many applications, such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in an environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios, including the fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this paper, a shadow-assisted tracking method for moving targets based on multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a Reverse Feature Pyramid Network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised Multi-level Discriminative Correlation Filters (MDCF) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.

For the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, the increasing imaging width results in a serious range ambiguity problem, which affects the performance of ground moving target indication (GMTI). In this article, a novel nonuniform subpulse coding (NSPC) scheme is proposed. It is characterized by resorting to range-frequency band resources and detailed coding design for each subpulse, enabling the beam auto-scanning in elevation. Also, the bandpass filtering and digital beamforming (DBF) technology with improved data reconstruction are utilized to realize the separation of subpulses and suppress range ambiguity. The NSPC technique exchanges the signal bandwidth for increasing swath without range ambiguity, and the coded subpulses can be directed to the prescribed regions, while skipping the invalid areas where the echoes are blocked. After that, through the robust principal component analysis (RPCA) method, the moving target detection is performed for each separated region without residual range-ambiguous interference. The proposed approach has been theoretically deduced in detail and the simulation experiments demonstrate its effectiveness.

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified$\varepsilon $-insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) has the advantages of wide beam coverage, long exposure time, and superior system flexibility. However, it is a challenge for GEO-BiSAR to efficiently acquire SAR images both with high resolution and wide swath due to the severe range and azimuth spatial variances. To deal with this issue, a modified Omega-K method is proposed in this paper. In the proposed algorithm, an equivalent range model associated with GEO-BiSAR configuration is built, where the equivalent parameters of the airborne radar receiver absorb the phase parameters corresponding to the GEO transmitter. Then, the Stolt interpolation is performed to realize the range-azimuth decoupling, achieving the linearization between range and frequency variables. Finally, a well-focused SAR image could be obtained after residual spatial variance error compensation. Simulations are presented to demonstrate the effectiveness of the proposed method.

Abstract When synthetic aperture radar (SAR) images are denoised by non‐local mean (NLM) algorithm, logarithmic transformation will lead to the loss of some image information. To keep the details and smooth the noise of the SAR images better, a new sub‐region NLM denoising algorithm with the statistical characteristics of SAR image is proposed in this paper. Firstly, the probability distribution image is generated by calculating the probability value of every pixel. Then the images can be divided into the heterogeneous region and the homogeneous region by the threshold obtained with the variation coefficient of the probability image. A new filtering weight using both the original and probability images is generated based on NLM in the heterogeneous region. The filtering weight is obtained using the probability image in the homogeneous region. This method fully considers the characteristics of noise in different regions. Multi‐SAR image experiments demonstrate the advantages of noise smooth and detail protection.

In this letter, a novel method is proposed to suppress the terrain fluctuation clutter based on adaptive broadening processing. In the proposed algorithm, the flat interference phase is first compensated according to the priori radar system parameters. Then, according to the space-time trajectory distribution of clutter edge, the level of clutter Doppler spread in virtue of crab effect is estimated by a cost function related to the clutter eigenvector matrix. Finally, after calculating the clutter suppression weight vector according to the broadened clutter subspace, the non-stationary ground clutter can be robustly rejected. The validity of the proposed method is verified by both the simulated and real-measured multichannel radar data.

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

Sea clutter magnitude distribution exhibits important guiding significance for the design of marine target detection algorithms and the selection of the constant false alarm rate (CFAR) detection threshold. In this letter, to deal with the limitation of the traditional generalized-K (GK) distribution in highly heterogeneous clutter scene, a modified GK (MGK) distribution is proposed for sea clutter magnitude. By combining the traditional GK and generalized Pareto distributions, the value range of power parameter and the applicable range of the distribution model are extended. The applicability of the proposed distribution model is verified by real-measured sea clutter data.

This letter deals with the issue of clutter suppression with the helicopter-borne single-channel rotating synthetic aperture radar (SCRoSAR). Commonly, obtaining an synthetic aperture radar (SAR) image and implementing the ground moving target indication (GMTI) simultaneously is a challenging task for the single-channel SAR (SCSAR), since, the clutter background covers most of the slow-moving targets in the SAR image. In this letter, we propose a novel clutter suppression method via the time-Doppler chirp varying (TDCV) approach for the SCRoSAR system. A traditional RoSAR imaging algorithm and a modified RoSAR-TDCV imaging algorithm are employed to generate an original image and a TDCV image from the same set of data, respectively. Benefits from the characteristics of the TDCV approach, the position of moving targets will be shifting along the range direction in the TDCV image. Meanwhile, the position displacement of stationary clutter scatterers is neglectable. Therefore, via image cancellation, the clutter background can be significantly suppressed. With the proposed approach, the RoSAR system is capable of imaging the stationary objects and revealing the moving targets, simultaneously. The experimental results with simulated data demonstrate the validation of our proposed method.

In remote sensing research, narrowband interference (NBI) suppression in synthetic aperture radar (SAR) is an urgent problem. Recently, many methods on NBI suppression are proposed via sparse recovery. In these methods, the optimal regularization constants are always hard to choose. Moreover, the number of NBI signals, equaling to the sparsity of the sparse vector, may change at different pulses, and the suppression performance might be reduced due to the regularization constants which control the sparsity of the sparse vector, are fixed. In this letter, aiming at these problems, a NBI suppression method for SAR based on sparse segmentation search (SSS) is proposed. Firstly, we build a non-convex optimization model without the regularization constant. Then the adaptive linear enhancer (ALE) is used to convert the non-convex model to a convex one. Finally, we solve this convex model and suppress NBI signals. The real-world SAR data experiments illustrate the effectiveness of the proposed method.

Deceptive jamming suppression in synthetic aperture radar (SAR) is an urgent problem. In this letter, an auxiliary-channel slow-time coding and change detection based deceptive suppression method is proposed. By coding pulses from the auxiliary channel, difference between two images, separately from the auxiliary and main channel, is appeared. Then, the change detection technology is utilized to reveal this difference as well as to form filters and to suppress deceptive jammings. Compared to some existing coding methods, in this method, deceptive jammings can be suppressed more thoroughly and a higher quality SAR image can be achieved. Results of false scene based simulation experiments demonstrate the effectiveness of the proposed method.

Random noise elimination acts as an important role in the seismic data processing. Moreover, protecting and recovering useful subsurface structure information are also significant. In this study, the S-mean that can obtain the geometric mean of the seismic traces on the symmetric positive definite (SPD) matrix manifold is adopted as a nonlinear filter for seismic denoising. Furthermore, S-mean has the best correlation with other elements based on the S-divergence due to the optimization of finding the S-mean on the SPD manifold. Therefore, the broken correlation features in noisy seismic data are compensated and maintained well, which can be conducive to describe the subsurface structures. Synthetic examples and field data applications qualitatively and quantitatively demonstrate the validity and effectiveness of the proposed workflow.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

In this letter, a novel method based on multifrequency pulse diversity (MFPD) is proposed to achieve high-resolution and wide-swath (HRWS) imaging by utilizing the displaced phase center antenna (DPCA) technique. In the MFPD mode, multiple waveforms from different frequency bands are transmitted through a single channel. Thus, within the same receive window, the echoes from different range regions correspond to different frequency bands, making it possible to separate the range ambiguous echoes in the range frequency domain. However, the azimuth sampling rate will be reduced in the MFPD mode, leading to the Doppler ambiguity. To this end, the MFPD-DPCA technique is utilized, which is capable of separating the range ambiguous echoes without loss of azimuth sampling rate. Moreover, the MFPD-DPCA technique can achieve high range resolution by spectrum splicing, which enhances the feasibility of super-high-resolution imaging. Finally, the HRWS imaging can be obtained by performing the traditional synthetic aperture radar (SAR) algorithm on the reconstructed unambiguous wideband echoes. The proposed method offers an alternative in system implementation but does not necessarily offer improved swath width over current classical HRWS-SAR methods. Numerical results corroborate the effectiveness of the considered HRWS imaging strategies in ambiguous scenarios.

In this paper, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several sparse reconstruction-based methods have been presented to solve this problem, the basis mismatch issue in sparse reconstruction theory will degrade the image reconstruction performance. To address this issue, this article proposes a novel 3-D imaging method for DLSLA 3-D SAR, which provides another idea for 3-D imaging through sparse recovery. It utilizes recovered full-sampled data to achieve cross-track dimensional imaging instead of using the under-sampled data directly as before. The Along-track-Height plane imaging is first finished by the range-Doppler (RD) algorithm and motion error compensation. Then, an advanced nuclear norm and low-rank matrix factorization (NU-LRMF)-based matrix completion (MC) algorithm and a vector reconstruction framework are built to achieve accurate recovery of full-sampled data. Finally, the cross-track dimensional imaging is completed with recovered full-sampled data by geometric correction and beamforming. Moreover, a fast two-stage iteration strategy for NU-LRMF (TS-NU-LRMF) is also presented to accelerate convergence. The robustness and effectiveness of the proposed 3-D imaging method are verified by several numerical simulations and comparative studies based on both the complex 3-D ship model and the simulated 3-D distributed scenario.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) usually uses a sparse and nonuniform linear array that often does not satisfy the Nyquist sampling theorem. Therefore, the cross-track dimensional imaging will fail with the traditional 3-D frequency-domain imaging algorithms. Several grid-based sparse reconstruction (GB-SR) algorithms have been presented to solve this issue. However, they assume that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. To address this issue, we propose a novel hyperparameter-free gridless-based sparse reconstruction (GL-SR) algorithm (i.e., 2-D adaptive iterative reweighted atomic norm minimization algorithm called 2-D IRAN) by a combination of the optimal covariance fitting criterion and atomic norm. It is a generalized model, while the other GL-SR algorithms (e.g., GLS, RGLS, and RAM) can be interpreted as the variants of 2-D IRAN. Moreover, since the interior-point method employed in toolboxes has high computational efficiency only for the small-scale matrix optimization problem, a fast implementation of 2-D IRAN via alternating direction method of multipliers (ADMM) is presented for the large-scale matrix optimization problem. Finally, we carry out extensive numerical simulations to demonstrate the advantages and effectiveness of 2-D IRAN for DLSLA 3-D SAR imaging based on the complex 3-D ship model and 3-D distributed scenario.

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression.

For a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system, it usually uses the digital beamforming technology. However, in practice, because of the influences of temperature, antenna pattern, receiving antenna, and other error factors, there may exist the range synchronization time errors, amplitude errors, and phase errors among different spatial channels. These nonideal factors will significantly degrade the multichannel data reconstruction performance, resulting in a smeared SAR image. To address this issue, in this article we propose a novel channel error correction algorithm based on the orthogonal projection theory. First, the optimal weight of each Doppler ambiguity component is calculated by the orthogonal projection. Then, the cost function is constructed based on the power maximization criterion, from which the channel phase errors can be obtained. Finally, the HRWS-SAR imaging can be finely realized after performing the channel balancing. Compared with the conventional phase error estimation method, the proposed algorithm does not require to perform the matrix eigenvalue decomposition, avoiding the signal leakage phenomenon under low SNR case. The effectiveness of the proposed algorithm is validated by both airborne and space-borne real SAR data.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time–frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

Since only the spatial feature information of ship target is utilized, the current deep learning-based synthetic aperture radar (SAR) ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale or rotations, and the complex background. To overcome these issues, a novel multidimensional domain deep learning network for SAR ship detection is developed in this work to exploit the spatial and frequency-domain complementary features. The proposed method consists of the following main three steps. First, to learn hierarchical spatial features, the feature pyramid network (FPN) is adopted to produce ship target spatial multiscale characteristics with a top-down structure. Second, with a polar Fourier transform, the rotation-invariant features of SAR ship targets are obtained in the frequency domain. After that, a novel spatial-frequency characteristics fusion network is then presented, which seeks to learn more compact feature representations across different domains by updating the parameters of sub-networks interactively. The detection results are obtained due to utilizing the multidimensional domain information, and we evaluate the effectiveness of the proposed method using the existing SAR ship detection data set (SSDD). The results of the proposed method outperform other convolutional neural network (CNN)-based algorithms, especially for multiscale and rotation ship targets under complex backgrounds.

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this paper proposed a homogeneous sample selection method based on a novel concept of Generalized Spatial Spectrum Density Function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of DEM data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy. But the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM); thereby, enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than other contrast methods.

Synthetic aperture radar (SAR) target recognition is essential for SAR image interpretation. It has been widely used in national defense and national economy. At present, the SAR image detection and recognition methods based on convolutional neural network (CNN) have problems such as insufficient extraction of the feature information of SAR image targets, false targets caused by the interference of complex backgrounds, and low detection performance. The main reason is that the feature extraction of CNN is a local operation in space and time, which ignores the correlation between pixels and regions and the dependencies between channels in SAR images. In this paper, a non-local channel attention network (NLCANet) SAR image target recognition method is proposed based on the GoogLeNet structure combined with asymmetric pyramid non-local block (APNB) and squeeze-and-excitation block (SEB). APNB is added to the GoogLeNet framework to capture more context information and enhance the correlation between pixels and regions. SEB is added to the Inception structure to become Inception-SEB (ISEB), through which channel dependencies based on the fusion of different scale features can be obtained. The experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset and the SAR ship detection dataset (SSDD) show that the proposed method improves the detection ability of targets in complex backgrounds and achieves better land-sea target recognition performance.

Owing to the highly maneuvering character involved in targets, the nonuniform 3-D rotation motions make the assumption that the image projection plane (IPP) is constant during coherent processing interval (CPI) invalid. In this work, an efficient approach is proposed in ISAR imaging for highly maneuvering targets with nonstationary IPP. First, to reasonably describe the mobility of a highly maneuvering motion target, the geometry and signal model with nonstationary IPP are established, where the high-order phase model is deduced to describe the 2-D spatial-variant phase errors. Second, based on the developed signal model, considering the cost function obtained via conventional image entropy with local extremum, the subarray averaging operation in conjunction with entropy is utilized to accelerate the global optimal convergence. Finally, the accurate 2-D spatial-variant phase errors compensation terms are generated to produce the well-focused ISAR images. Compared with existing methods, the main advantages of this work are: 1) the geometry and signal model of the target with nonstationary IPP are established; 2) the subarray averaging operation in conjunction with image entropy is utilized to accelerate the global optimal convergence; and 3) the high-order signal model is derived to present the 2-D spatial-variant phase errors. Several numerical experiments using simulated data and electromagnetic data are conducted to demonstrate the validity of the proposed algorithm and signal model.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

High computational complexity and phase errors (PEs) are the main limitations of time-domain (TD) synthetic aperture radar (SAR) imaging algorithms. Accelerated fast backprojection (BP) (AFBP) algorithm avoids interpolation through wavenumber spectrum connection and is an efficient fast TD imaging algorithm. In order to deal with the image defocusing problem caused by PEs effectively and ensure rapid imaging, a TD autofocus method is proposed in this article, which is based on generalized sharpness metrics and the AFBP imaging model. The autofocus method is divided into two stages. First, for each subaperture (SA), the PE estimation model is established in unified polar coordinate (UPC), where the strong-scattering range-cell pixels are chosen to reduce memory burden and avoid repetitive imaging. The PE estimation is converted into a nonconvex optimization problem. Then, the genetic algorithm (GA) and the maximizing-maximum-pixel-value (MMPV) method are used to estimate the PEs. Second, SA images’ matching and constant PE’s compensation are performed to eliminate the residual PEs. The full-aperture well-focused image is obtained by the coherent accumulation of SA images. The effectiveness of the proposed method is proven by the results of simulation and real SAR data processing.

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with$N$-dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing.

To maximize the signal-to-interference-plus-noise ratio (SINR) under a constant-envelope constraint, an efficient joint design of the transmit waveform and the receive filter for multipleinput multiple-output (MIMO) radars is essential. In this paper, we propose a novel optimization framework to solve the resultant non-convex problem on a Riemannian product manifold. Based on the Riemannian structure of the formulated manifold, three Riemannian gradient-based methods are proposed to deal with the reformulated problem efficiently. The proposed algorithms provably converge to a local optimum from an arbitrary initialization point. Numerical experiments demonstrate the algorithmic advantages and performance gains of the proposed algorithms.

This letter deals with the motion parameter estimation and focusing for ground moving targets in synthetic aperture radar (SAR) imagery. In the proposed algorithm, after range compression, the echo signal of a moving target is first transformed into the range-frequency and Doppler domain. Then, the target signal is characterized as an inclined trajectory after applying the correlation operation with respect to the divided Doppler subaperture data. Finally, target motion parameter estimation and focusing can be effectively accomplished based on the Doppler axis rotation transform (DART). The effectiveness of the proposed algorithm is validated by both simulated and real airborne/spaceborne SAR data.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

The rank of the sparse signals brought by multiple measurement vectors (MMV) augments the performance of joint sparse recovery. In general, suppose the sparsity level k is less than or equal to [rank(X)+spark(A)-1]/2, the sparsest solution of the MMV problem is unique and recoverable via various methods. It is shown in this letter that the unique solution of the sparsity level k up to spark(A)-1 actually exists in a measure theoretical point of view. More specifically, even when [rank(X)+spark(A)-1]/2 ≤ kA), the sparsest solution toAX=Yis still unique with full Lebesgue measure in every k-sparse coordinate space. This phenomenon is fully confirmed by the MMV tail-l2,1minimization technique. Furthermore, the phenomenon that the traditionall2,1minimization actually fails to recoverXwith k ≥ [spark(A)-1]/2 is investigated from the same perspective of measure theory. Extensive numerical tests conducted by the MMV tail-l2,1minimization andl2,1minimization are demonstrated to confirm the findings. The tail minimization procedure exhibits the most prominent effectiveness for the larger sparsity levels among all known techniques.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

By transmitting a single frequency‐shifted waveform, coherent frequency diverse array (FDA) can provide a simple way to realize full spatial coverages with stable gains. Owing to the range‐angle dependency in coherent FDA, the authors implement a two‐dimensional angle‐time matched filter to perform equivalent transmit beamforming and matched filtering simultaneously. However, such filter structures merely control main‐lobes of equivalent transmit beams towards target directions. It fails to form nulls at interference directions. Moreover, traditional adaptive beamforming by designing adaptive weight vectors are no longer applicable. Additionally, they find that the range resolution scales linearly with the element number. To tackle these issues, a space‐time matched filter (STMF) in combination with the hybrid coding technique is proposed. Aiming at mitigating interferences, the STMF is designed with a formulation of quadratically constrained quadratic programing. By relaxation of quadratic constraints, the hard non‐convex problem can be turned into the second‐order cone programing to obtain optimal filter parameters. Furthermore, the hybrid coding technique is devised to jointly improve the range resolution. Numerical experiments of both two‐dimensional range‐angle outputs and one‐dimensional range profiles via filtering are provided, which demonstrate that the STMF with hybrid coding can effectively suppress sidelobe interferences with a range resolution enhancement.

Coherent frequency diverse array (FDA) can provide the full spatial illumination with a stable transmit gain by employing a single frequency‐shifted waveform. However, the authors find that the range resolution is scaled linearly with the number of elements. In this work, the problem is first quantitatively analysed through mathematical derivation. To solve the issue, a subarray‐based coherent FDA transmitting pulsed linear frequency modulation signals is proposed. The essence of the subaperture technique is to partition the transmit antenna array into multiple regular or irregular subarrays, wherein an identical carrier frequency is utilised in each subarray. Meanwhile, distinct carrier frequencies are adopted between subarrays. Moreover, the multi‐dimensional ambiguity function is studied to assess the properties including the range resolution, spatial coverage and sidelobe level. Simulation results demonstrate that the proposed approach has superiorities in range resolution enhancement and range sidelobe reduction.

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

This letter addresses the long-time coherent integration issue for weak maneuvering target detection in a staggered pulse repetition interval (PRI) radar system. Due to the random phase fluctuation caused by the varied PRI, the complex range-azimuth coupling effects will significantly deteriorate the maneuvering target detection performance. In this letter, the nonuniformly sampled signal is efficiently reconstructed into a uniform grid based on the filterbank framework, where the uniformly sampled spectrum of a fast-moving target with Doppler ambiguity can also be reconstructed. After compensating the coupling influence caused by target motion, a moving target can be finely focused. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In order to maximize the signal-to-interference-plus-noise ratio (SINR) under a constant-envelope (CE) constraint, a fast and efficient joint design of the transmit waveform and the receive filter for colocated multiple-input multiple-output (MIMO) radars is essential. Conventional joint optimization is performed using nonlinear optimization techniques such as the semidefinite relaxation (SDR) algorithm. In this letter, we propose a novel manifold-based alternating optimization (MAO) method, which reformulates the waveform optimization subproblem as an unconstrained optimization problem on a Riemannian manifold. We present the geometrical structure of the feasible region and derive the explicit expressions for the Riemannian gradient and the Riemannian Hessian, thus the reformulated optimization could be solved by using the Riemannian trust-region (RTR) algorithm. Numerical experiments demonstrate that the proposed method has faster convergence with reduced computational cost compared with conventional SDR-based algorithm in Euclidean space.

Source number detection is a critical problem in array signal processing. Conventional model-driven methods e.g., Akaikes information criterion and minimum description length, suffer from severe performance degradation when the number of samples is small or the signal-to-noise ratio is low. In this letter, we exploit the model-aided based deep neural network to estimate the source number. Specifically, we propose two eigenvalue based networks, i.e., a regression network (ERNet) and a classification network (ECNet), for source number detection, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the label of the networks, respectively. Furthermore, ERNet and ECNet can be easily generalized to handle coherent sources by adopting, e.g., the forward-backward spatial smoothing technique. Numerical results are included to showcase the remarkable improvements of ERNet and ECNet over the existing methods.

Clutter background suppression and velocity estimation for moving targets are two critical problems in synthetic aperture radar-ground moving target indication (SAR-GMTI). A robust principal component analysis (RPCA) method is used to separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds by using amplitude information in the image domain. However, the nonsparsity of the moving target echoes limits the performance of the RPCA in SAR-GMTI, and the velocity of the moving target cannot be estimated since the phase information is destroyed by the soft-thresholding operator in the RPCA process. To solve these problems, a novel moving target detection method based on RPCA (NRPCA) for SAR systems is proposed in this article. An atomic norm-based optimization program is first constructed to transform the data sparsity requirement into a moving target sparsity requirement. Although this optimization program is NP-hard, it is transformed to semidefinite programming by relaxation. Furthermore, accurate velocity estimation is performed using dual function theory and the alternating direction method of multipliers (ADMM) algorithm while the selection of the sparsity order k is avoided. Simulations and analyses based on experimental data illustrate the effectiveness of the proposed method.

Clutter background suppression is a critical problem in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, a great quantity of secondary data is not easily acquired in heterogeneous environments. To solve the problem of clutter suppression, a method based on nonlocal self-similarity-robust principal component analysis (NSS-RPCA) is proposed for airborne SAR systems. First, discrete clutter is separated from the echo data by RPCA after range pulse compression. Second, similar blocks of the residual-clutter background are extracted to overcome the training sample limitation using the NSS method in the 2-D time domain. Third, subcovariance matrices are structured by the similar blocks, and the subcovariance matrix is stacked into a tensor. Then, the subclutter covariance matrix can be obtained from the stacked subcovariance matrix tensor by RPCA, where the residual-clutter tensor is of low rank and the target tensor is sparse. Finally, the residual clutter can be suppressed by the subclutter covariance matrix. In this manner, the source of independent identically distributed (IID) samples will be increased significantly without aperture loss by the proposed method. Simulation and analysis based on the experimental data illustrate the effectiveness of the proposed method.

A range-ambiguous clutter suppression approach based on extended azimuth phase coding (EAPC) is proposed to handle the range ambiguity of the multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system for ground moving target indication (GMTI) application. The echoes from different ambiguous range regions can be well separated in the transmit spatial frequency domain by properly designing the EAPC shift factor. In the sequel, a set of transmit filters are employed to extract the echoes of each ambiguous region independently. Then the azimuth deramp operation is applied to the extracted data to focus the target energy of the desired region while the residual target energy of other regions is still smeared due to the mismatched reference function. After this, the adaptive matched filtering algorithm is adopted to suppress the clutter and detect the moving target. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for range-ambiguous clutter suppression and ground moving target detection.

Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit-receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit-receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, an efficient range-Doppler domain ISAR imaging method for rapidly spinning targets is proposed in this article. First, echo signal is transformed into range-Doppler domain and its precise analytical expression is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius. By doing so, the energy is concentrated in the same range cell. After that, the high-order phase terms of the signal are compensated and the CLEAN technique is also applied to reduce the sidelobes of a strong scatterer. Finally, 3-D ISAR image of the spinning target is reconstructed by projecting the spatial parameters to 3-D cylindrical coordinates. Furthermore, in this article, the output signal-to-noise ratio (SNR) gain, anti-noise performance, the mismatched phase error, and the computational complexity analyses are also provided. Compared with existing approaches, the proposed method has advantages in the computational complexity and low SNR environment thanks to the only 1-D search and the coherent integration gain obtained. Both the theoretical derivations and the simulated results demonstrate the effectiveness of the proposed method.

Synthetic aperture radar (SAR) operating in an along-track interferometric (ATI) mode has the advantage of minimum detectable velocity (MDV) in active marine target detection. However, most of the conventional ATI detectors fail to identify the marine targets cruising with blind speeds, in which case the interferometric phases of these targets are closely distributed around those of the sea background due to phase wrapping and aliasing through ATI processing. To address this issue, we propose a method to detect the active marine targets based on complex interferometric dissimilarity for dual-channel ATI-SAR systems. First, an interferometric bilateral filter is designed to smooth the random noises and locally adapt to the spatial structure of the interferogram for measuring the interferometric magnitude and phase. Then, the target detection metric is constructed based on the complex interferometric dissimilarity between the marine targets and the sea background. By adaptively regressing into a magnitude-based test toward the blind-speed targets, the target detection metric can mitigate the blind-speed detection problem and thus yield a satisfactory detection result. Furthermore, this metric is of a constant false-alarm rate (CFAR), and its probability density function (pdf) is derived to facilitate the detection threshold computation. Finally, both the simulated and real-data processing results are given to validate the superiorities of the proposed method.

Much research from open literature shows that polarization diversity can provide another dimension which may be exploited to improve the performance in ground moving target indication (GMTI), compared with space-time adaptive processing (STAP). In this article, we report the multichannel synthetic aperture radar (SAR)-based GMTI (SAR-GMTI) experiment and its preliminary results with a N-SAR system which is an airborne quadrature-polarimetric (quad-pol) radar system. First, the joint polarization-space adaptive processing (JPolSAP) is performed in the image level, but two suboptimal versions of JPolSAP, where the polarimetric matched filter (PMF) vector and the full-one vector are exploited to substitute for the polarimetric steering vector, respectively, are evaluated since the polarimetric steering vector of the moving target is unknown precisely in practical applications. Then, considering the computational complexity and lack of secondary data in a inhomogeneous environment due to high degrees of freedom of the JPolSAP processor, two cascade processors are evaluated, including the polarization enhancement that uses PMF and noncoherence integration detection (NCID) technique. Furthermore, we utilize the polarization information to accomplish SAR terrain classification, and subsequently secondary data from the same scattering type clutter can be obtained for clutter suppression under the guidance of polarization classification results as a priori knowledge. Finally, the experimental results demonstrate that: 1) the suboptimal JPolSAP processor with PMF steering vector can effectively enhance GMTI performance about 13 dB (or even up to 5 dB) relative to the worst (or best) single-polarization (S-pol) processor case and has the best robustness compared with the one with full-one steering vector; 2) polarization enhancement using PMF also obtains a good output gain of polarization filter, especially for quad-pol enhancement, which gains up to 2-3 dB with respect to the best output of S-pol processor, and the NCID technique can obtain good performance of moving-target detection; and 3) under the guidance of polarization classification results, the capability of clutter suppression can improve even up to 15 dB with respect to the one without classification.

The low-rank approximation problem has recently attracted wide concern due to its excellent performance in real-world applications such as image restoration, traffic monitoring, and face recognition. Compared with the classic nuclear norm, the Schatten-p norm is stated to be a closer approximation to restrain the singular values for practical applications in the real world. However, Schatten-p norm minimization is a challenging non-convex, non-smooth, and non-Lipschitz problem. In this paper, inspired by the reweighted ℓ1 and ℓ2 norm for compressive sensing, the generalized iterative reweighted nuclear norm (GIRNN) and the generalized iterative reweighted Frobenius norm (GIRFN) algorithms are proposed to approximate Schatten-p norm minimization. By involving the proposed algorithms, the problem becomes more tractable and the closed solutions are derived from the iteratively reweighted subproblems. In addition, we prove that both proposed algorithms converge at a linear rate to a bounded optimum. Numerical experiments for the practical matrix completion (MC), robust principal component analysis (RPCA), and image decomposition problems are illustrated to validate the superior performance of both algorithms over some common state-of-the-art methods.

In order to reconstruct the linear frequency modulated (LFM) signal, such as radar signal due to the complexity. A novel parameter estimation method based on a modified convolution kernel function (MCKF) is proposed for multi-component LFM signal in this paper. The method has fewer external cross-terms and light computational burden because of non-searching operations. Moreover, it is robust against additive noise. Finally, simulated and real data results confirm the proposed method.

Ground moving target indication (GMTI) is an important research field in multichannel-synthetic aperture radar systems (SAR). The robust principal component analysis (RPCA) method can separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds in image or video data. As the correlation coefficient variation caused by phase different between channels is weak for SAR, it results in that the conditions of applying RPCA method cannot be satisfied. To solve this problem, an improved moving target detection method based on RPCA is proposed in this paper. By analysis, the phase different between channels is transformed to the shifting in time-frequency domain. Therefore, the correlation coefficient is decrease between channels, which means that the sparse matrix of moving targets is extracted with higher probability. Simulation and analysis illustrate the effectiveness of the proposed method eventually.

Conventionally, near‐field scattering effect was considered in antenna measurement, which requires a great amount of measuring efforts and re‐measurement if the operational platform has changed. In this study, the authors consider the near‐field scattering problem in the framework of adaptive array beamforming theory, which avoids re‐measurement and can be adaptive to the arbitrary scenario. Generally, the near‐field scattering can result in severe performance degradation in adaptive beamforming applications. They propose a robust adaptive beamforming approach that can maintain the performance in the presence of near‐field scatterers with unknown scattering coefficients. In the authors’ approach, the near‐field scatterering signal component is incorporated into the presumed steering vector. Thus, it is treated as useful information in this study instead of nuisance interference in the literature. In particular, the properties of the far‐field direct‐path signal and near‐field signal are explored and the large uncertainty set is divided into two small ones describing the far‐field and near‐field steering vectors, respectively. Simulation examples are provided to show the performance improvement by making use of the near‐field scattering signals.

Space–frequency modulation (SFM) signal is a potential waveform for multifunction radar with high degrees of freedom of space, time, and frequency. However, the introduction of the additional communication function will modulate the transmitting signal, which will severely deteriorate the autocorrelation function (ACF). Here, a modified CLEAN method is proposed to eliminate the influence of undesired sidelobes in ACF on the air target detection. In the proposed method, undesired sidelobes are treated as extra features of real target to obtain more precise estimation of its complex reflection coefficient. Moreover, by considering about the sparsity of air targets, the authors employ the sparse representation method to estimate the response of the current strongest target. Then the target occlusion is eliminated by iterative cancellation. Simulation results demonstrate that the proposed detector is reliable and effective for the SFM‐based integrated system.

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments.

This letter addresses the coherent integration problem for the moving target detection in a frequency agile radar system. Due to the random phase fluctuation caused by the carrier frequency random hopping, the complex range-azimuth coupling effects will significantly deteriorate the target integration performance. In this letter, echoes are classified into different bursts according to the carrier frequencies, and then keystone transform (KT) is applied to correct range walk in every burst. After compensating the range offsets among different bursts and rearranging the signal returns, the scaled transform is constructed to remove the residual coupling between the agile carrier frequency and slow-time. Finally, a moving target can be well-focused in the frequency-velocity domain. Simulated results are provided to validate the effectiveness of the proposed algorithm.

A rotating synthetic aperture radar (RoSAR) can image the environment in 360° on a stationary platform because it uses rotating antennas. The range-variant distortion in azimuth is evident in the RoSAR system, which degrades the imaging quality, particularly in high-resolution situations. In this letter, a new chirp z transform (CZT) imaging algorithm is developed to remove the range-variant distortion in azimuth for helicopter-borne RoSAR systems. Based on a fourth-order approximation of the slant range model, a precise expression of the 2-D spectrum of the echo is derived via the series reversion method. The spatial-variant characteristics of the range cell migration term and the second range compression term are analyzed and compensated. Then, according to the analysis on the range-dependent output sample spacing variation in azimuth, which is caused by the RoSAR configuration, an efficient CZT is proposed to remove the range-variant distortion in azimuth. The experimental results with simulated data are provided to clearly demonstrate the proposed approach.

In this letter, a sensor localization technique for highly deformed partially calibrated arrays with multiple moving targets is proposed. The deformed array is divided into several subarrays. The first subarray is composed of the pre-calibrated sensors, whereas the sensors in the other subarrays are uncalibrated. The positions of the sensors are estimated from the phase differences between the pre-calibrated sensors and the uncalibrated sensors. The phase ambiguities caused by the highly deformed sensor positions can be solved using the subspace orthogonality and the movement of multiple targets. Simulation results evaluate the performance of the proposed method, and the Cramer-Rao bounds are compared.

A synthetic aperture radar (SAR) system is severely affected by radio frequency systems, such as TV and cellular networks. Previous studies showed that narrowband radio frequency interference (RFI) is low rank and used the nuclear norm as a low-rank regularization to extract the RFI from the received signal. However, the nuclear norm is not an appropriate approximation of the true rank function. Hence, in this paper, the reweighted matrix-factorization (RMF) algorithm and the matrix-factorization decomposition (MFD) algorithm are proposed to suppress narrowband RFI for SAR systems, where the RMF algorithm uses the reweighted scheme to approximate the rank function, while the MFD algorithm restrains the upper bound of the rank as a prior condition. Moreover, the introduction of the MF scheme dramatically decreases the computational complexity and efficiently suppresses RFI. In addition, we further show that the sparse regularization of the useful signal (i.e., the useful SAR echo) not only protects the strong scatterers of the useful signal but also avoids low-rank overfitting. We employ the real SAR signals of both the sparse scene and the nonsparse scene with the measured RFI to verify the effectiveness of the proposed methods, and the proposed methods outperform the other methods for RFI suppression.

Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks. Since the narrowband radio frequency interference (RFI) has a stable frequency in a snapshot sequence, it has a low-rank property that can be used to substract RFI from the received signal. The nuclear norm is a common convex relaxation to constrain the rank, but it is optimized by the singular value thresholding (SVT) algorithm, which uses a single threshold to treat all singular values and greatly over-punishes large singular values. Hence, in this paper, we propose two methods, the reweighted nuclear norm (RNN) algorithm and the reweighted Frobenius norm (RFN) algorithm, to separate the RFI and the useful signal. The RNN and RFN minimization problems are the approximations of the real rank function, which can protect large singular values and restrict the rank. As a result, the RFI is accurately extracted and the useful signal is successfully protected. Also, we strictly derive the closed-form solutions of the RNN and RFN minimization problems for complex radar signals, and we also employ downsampling to extract the mainband of the signal spectrum to speed up the convergence. Real SAR data is applied to demonstrate the effectiveness of the proposed methods for RFI suppression.

Radio-frequency systems, such as TV and cellular networks, severely interfere with synthetic aperture radar (SAR) systems. Narrowband radio-frequency interference (RFI) has a special low-rank property in the received signal matrix, because it performs like a sinusoid with nearly invariant frequency as the slow time proceeds. Exploiting this special property, in this paper, we divide the received signal matrix into several small matrices, in each of which the RFI is also low rank. Without losing the connection between these small matrices, we stack them into a three-mode tensor to separate the low-rank RFI tensor and recover the informative signal tensor. Previous studies employed the nuclear norm to regularize the low-rank RFI, which is not a good choice. Hence, we propose two reweighted algorithms, the reweighted tensor nuclear norm (RTNN) and the reweighted tensor Frobenius norm (RTFN) algorithms, to approximate the rank function in a tensor and accurately extract the low-rank RFI tensor from the received signal tensor. As a result, the introduction of the tensor structure dramatically decreases the computational cost. Furthermore, the reweighted scheme helps suppressing the RFI and recovering the useful signal with excellent performance. Finally, real SAR data with measured RFI is employed to demonstrate the effectiveness of the proposed methods for RFI mitigation.

In this paper, a new algorithm is presented to image ground moving targets in a synthetic aperture radar (SAR) system based on range frequency reversal transform-fractional Fourier transform (RFRT-FrFT). In this algorithm, a range compressed signal is initially transformed into the range frequency domain and then RFRT is proposed to directly compensate the range migration via multiplying the signal in the range frequency domain by its reversed data according to the equal interval sampling of range frequency variable, which can significantly decrease the computational complexity in target envelope migration elimination. Then, FrFT is applied to accomplish the target motion parameter estimation after range migration alignment. Finally, a ground moving target is well focused after motion compensation. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

To better interpret the inverse synthetic aperture radar (ISAR) imaging results, it is highly desirable to present them in the homogeneous range-cross-range domain, rather than the conventional range-Doppler (RD) domain. This process is referred to as cross-range scaling and the rotating angle velocity (RAV) of the moving target must be estimated first to achieve that goal. In this paper, an efficient cross-range scaling approach based on 2-D discrete wavelet transform (2D-DWT) and pseudopolar fast Fourier transform (PPFFT) is developed. To be exact, first, 2D-DWT is applied to two sequential ISAR images to obtain the dominant feature points based on the fact that the ISAR images are usually redundant for estimating RAV. By doing so, the data dimensional reduction and noise suppression are also realized. After that, second, via the efficient PPFFT, two sequential RD ISAR images are mapped into the pseudopolar coordinate to convert the rotational motion into the translational motion along the pseudo angle direction. Finally, to estimate the RAV, a new normalized correlation cost function is constructed and the Golden section algorithm is employed to efficiently find the optimal RAV. Compared with the conventional methods, the advantages of the proposed method are threefold: 1) the rotation center of a target is no longer required prior; 2) without the interpolation operation and the utilization of data dimensional reduction via 2D-DWT, the computational complexity of the proposed method is significantly reduced;and 3) the accurate RAV estimation is achieved in the case of low signal-to-noise ratio condition. The results from both the simulated and the measured data demonstrate that the proposed approach outperforms the state-of-the-art algorithms in terms of the estimation accuracy and computational complexity.

Helicopter‐borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter‐borne ROSAR wavenumber‐domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA‐based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well‐focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter‐borne ROSAR imaging.

In recent years, many researchers have focused on the automatic target recognition problem for high-resolution synthetic aperture radar (SAR) systems. Most have directly employed the training data as the dictionary, which introduces error from speckle noise. In this letter, a joint low-rank and sparse multiview denoising (JLSMD) dictionary is generated, which combines multiview training samples for denoising. To extract the dictionary, we fully consider the low-rank property of multiview target images and the sparsity of speckle noise for SAR systems. The designed dictionary is more accurate than the training data in representing the target. With the help of the proposed JLSMD dictionary, we develop three algorithms based on the sparse representation classification and the support vector machine approach. We carry out experiments on the moving and stationary target acquisition and recognition public data set to evaluate the excellent performance of the proposed methods against several state-of-the-art methods, including deep learning methods.

Geosynchronous orbit synthetic aperture radar (GEO SAR) plays an important role in wide-area surveillance and the continuous coverage of areas containing targets of interest. However, the relative position errors of large aperture arrays will distort the antenna pattern, which significantly degrades the target detection performance in a GEO SAR system. To address this issue, most of the conventional calibration methods are focused on the independent errors, without consideration of the parametric error model, which may increase the position estimation errors. To solve this problem, an efficient calibration algorithm for position errors in a GEO SAR is proposed in this letter. For the large antenna arrays, the parametric error model is first established. Then, the calibration method is performed to estimate the parameters of the position error model. Based on this, the accurate position errors can be obtained by the least-squares algorithm. Compared with the conventional methods, the target detection performance of a GEO SAR system can be significantly improved after the precise array position error compensation by the proposed algorithm. Moreover, the proposed algorithm transforms the estimation from 3-D positions to the finite parameters of the error model, which can considerably decrease the computational complexity and obtain a more accurate estimation of the position errors simultaneously. Several simulated results are presented to validate the proposed algorithm for the position error correction in a GEO SAR system.

The synthetic aperture radar (SAR), as a wideband radar system, operates over a large frequency band ranging from the low very high frequency to millimeter waves. It often overlaps in frequency with other radio-frequency systems including radio, television, and cellular networks. Therefore, radio-frequency interference (RFI) suppression is the severe test for SAR systems. Recently, some methods are proposed to suppress the RFI based on the sparse recovery in range-frequency domain and low-rank extraction in the azimuth dimension. However, all the previous methods exploit one property of the RFI, which may leave the room for performance improvement. Hence, in this paper, we propose three methods to jointly exploit the sparsity and low-rank property of RFI. We first include the sparse term and low-rank term of the RFI in the objective function to separate the RFI and the useful signal, which is defined as the joint sparsity and low-rank property method. It has better performance but heavier computational burden than the algorithm employing only the low-rank property. Then, we use row-sparse (RS) concept in lieu of the two properties, since the narrowband RFI has relatively stable frequencies during the synthetic aperture time. The RS method avoids the low-rank optimization and dramatically decrease the computational burden. Also for the real radar system, the sampling frequency is commonly larger than the frequency bandwidth. Therefore, there are some redundant data in the received signal. We exploit both the downsampling operation and the RS concept to speed up the convergence, which is called the fast row-sparse (FRS) method. The FRS method can further eliminate the out-of-mainband RFI. The real SAR data are provided to demonstrate the effectiveness of the three proposed methods.

Synthetic aperture radar (SAR), as a wideband radar system, is easy to be interfered by radio frequency systems, such as the radio, television, and cellular works. Since the narrowband radio frequency interference (RFI) has a relatively fixed frequency during the synthetic aperture time, it is removed as a low-rank term of the received signal in recent research. In this paper, we employ a novel “low-rank + sparse” decomposition model to extract the low-rank RFI and protect the strong scatterers of a useful signal, which is explicit and more efficient than the previous augmented Lagrange function model. Because the radar signal is complex, we exploit soft thresholding instead of hard thresholding in the Go Decomposition algorithm, which is defined as the revised traditional decomposition (RTD) method. Soft thresholding can recover the phase term correctly for a further focused image. Both the previous augmented Lagrange method and the proposed RTD method need to search the values of user parameters with high computational complexity. In order to eliminate the bother of tuning user parameters, a parameter-free decomposition (PFD) method is proposed to adaptively estimate the user parameters. Also, by considering the property of the useful signal, the PFD method protects the useful signal with adaptive thresholds for each snapshot. It has a better performance for RFI suppression, but costs slightly more computational time compared with the RTD method. The real SAR data and the measured RFI are provided to demonstrate the correctness of the proposed methods.

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper.

In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time-frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

This paper develops a framework based on enhanced shadow-aided decision for multichannel synthetic aperture radar-based ground moving target indication system according to the relationships between the moving target and its shadow information in position, dimensions, and intensity. As a sort of feature information available, the moving target shadow may improve the ground target detection performance. A critical precondition for shadow utilization is to obtain the good detection performance for the moving target shadow. However, shadow detection performance will deteriorate inevitably as a result of target motion that blurs its shadow. To address this issue, a knowledge-aided shadow detection algorithm with adaptive thresholds is proposed to improve the shadow detection performance in the developed framework. Furthermore, the theoretical performance analysis is performed, which indicates that the proposed knowledge-aided shadow detection algorithm has a better performance than that of the conventional shadow detection algorithm with a fixed threshold. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for extended ground moving target detection.

Conventionally, a single-channel synthetic aperture radar (SC-SAR) system can hardly detect weak moving targets simply. In this paper, a time-Doppler chirp-varying (TDCV) filter is proposed for ground moving target indication and parameter estimation with the airborne SC-SAR system. The proposed method is easy to implement and mainly includes three steps. First, a traditional 2-D frequency range-Doppler algorithm is used to generate an original image. Second, the second-order range cell migration (RCM) phase term is partly compensated in the range frequency and azimuth time domain, and the rest of second-order RCM phase term is compensated in 2-D frequency domain. The whole processing, which is referred to as the TDCV approach, is employed to acquire a new TDCV image. Third, compared the original image with the new image, the clutter scatterers are nearly motionless while the moving targets are translated along the range direction due to their nonzero radial velocities. After the cancellation between two normalized images, the clutter background would be significantly suppressed since the two images generated by the same data. As a result, the moving targets can be indicated and the range difference of the moving target between two images can be exploited to estimate their radial velocities. The results obtained by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper.

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range-Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.

For radar target detection, the clutter distribution model needs to be identified first. The goodness of fit (GoF) between the original data and the assumed distribution can be used to choose the proper distribution model. Generally, the GoF is obtained using data histogram and theoretical distribution curve, and then the distribution model is judged via GoF. However, when the sample number is small, the histogram is rough and fluctuating, affecting the analysis of GoF. For the small sample, the graphical characteristic is obtained with the sample data to choose the most fitting distribution to the data in this paper. The graphical characteristic is acquired by a simpler process, that is, the original data are directly set as the test statistics, avoiding computing and sorting of other statistics. In this paper, the real airborne circular synthetic aperture radar data under different scan angles are analyzed using the GoF corresponding to histogram and graphical GoF, respectively. The results show that when the sea-clutter data histogram is close to two distributions, a more fitting distribution model may not be obtained by traditional GoF, but can be acquired by graphical representation. In addition, the sea data with different sight angles have different match properties. It is seen that the sea data are closer to the Rayleigh distribution in side-looking mode than that in big squint-angle mode, while the Weibull distribution and K distribution show equal fitting performance to sea clutter under variant radar sight angles.

In this paper, a novel method for ground moving target indication (GMTI) and parameter estimation is proposed with single channel of synthetic aperture radar (SAR) system. The proposed method uses the varying Doppler chirp rate to get a new image, where the clutter scatters are motionless and the moving targets are translational along slant range direction compared with the original image. As a result, the moving targets can be indicated and the radial velocities of moving targets can be estimated by the offset of range cells. Numerical examples are illustrated to demonstrate the correctness of the proposed method.

Adaptive detection of radar target embedded in homogeneous Gaussian disturbance is addressed, by exploiting the persymmetric covariance matrix. On the basis of the decision schemes of generalised likelihood ratio test (GLRT), an adaptive persymmetric detector with constant false alarm rate property is designed, which can mitigate the demanding requirement of secondary data. Furthermore, the expressions for the probabilities of the false alarm and detection are derived, and the validity of them is confirmed by Monte Carlo simulations. The assessment results show that the proposed detector outperforms the conventional unstructured GLRT, the structured persymmetric adaptive matched filter and the persymmetric Rao detector, especially in the training‐deficient scenarios.

Adaptive ground moving target indication (GMTI) algorithms based on the sample matrix inversion require the availability of a secondary data (training data) set to determine the adaptive filter. A polarization-aided GMTI method is devised in this letter for selecting this training data, which could improve the detection performance in heterogeneous environments. In particular, improved classification results are first obtained with the proposed polarization-space 2-D Wishart classifier, which are then employed in a generalized inner product algorithm to select the secondary data. The proposed scheme is able to provide a better choice of secondary data, resulting in considerable improvement in detection performance. Numerical results are provided to show the effectiveness of the proposed method.

In this letter, a reduced-rank beamforming method is presented based on knowledge-aided joint iterative optimization. The proposed adaptive reduced-rank processing is realized by joint optimization for the reduced-dimension matrix and beamforming weight vector. Meanwhile, recursively updating the prior covariance matrix using the spatial spectrum reconstruction technology and the weighted processing improve the estimation precision of the array covariance matrix. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output signal-to-interference-plus-noise ratio (SINR) of the adaptive beamformer under the condition of few samples.

Clutter suppression and ground moving target indication (GMTI) are challenging tasks for multichannel synthetic aperture radar (MC-SAR) systems. In recent years, robust principal component analysis (RPCA), such as the augmented Lagrange multiplier method (ALM) and go decomposition (GoDec) algorithm, has drawn considerable attention for its excellent performance in distinguishing the different parts from a set of correlative database. In this letter, an efficient along-track interferometry GoDec (ATI-GoDec) approach is proposed for GMTI in MC-SAR systems under a strong clutter background. The proposed method can be separated into two sections: the predetection and the postdetection. The predetection by an efficient ATI RPCA method can decrease missing targets, and postdetection with a novel magnitude and phase (M&P) detection has the ability to reduce the false targets. As a result, the proposed method can provide a more robust performance by a comparison to the traditional ATI detection. It can also widen the tolerant values of the preset cardinality and decrease the probability of false alarm compared with the conventional GoDec algorithm. Moreover, the proposed method only takes several iterations to reach the convergence by solving the optimization problem of the RPCA model, which makes it more efficient than the previous RPCA methods. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this letter.

With the observation distance of the radar increasing, the multichannel high-resolution synthetic aperture radar system may suffer from the reduction of the target signal-to-noise ratio, which leads to degradation in the detection performance for ground moving target indication (GMTI). Fortunately, the shadow feature, apart from the amplitude and interferometric phase of a moving target, may be available to improve the performance for target detection. In this letter, according to the geometric relationships between the moving object and its shadow in position and size, a shadow-aided method for GMTI is proposed. In addition, an efficient shadow detection method based on multifeature fusion is discussed to improve the shadow detection performance. Finally, numerical simulation results show that the shadow-aided method has a better detection performance, compared with the traditional detection algorithms.

This letter addresses the problem of high-resolution radar detection in interference and nonhomogeneous noise. The target signal and interference lie in two linearly independent known subspaces, but with unknown coordinate. The noise is modeled by a compound-Gaussian process with unknown covariance matrix and random texture. According to the two-step generalized likelihood ratio test-based design approach, we derive a distributed target detector. Numerical examples show that the proposed detector can provide better detection performance than their counterparts in nonhomogeneous environments.

In synthetic aperture radar (SAR) imaging of a ground moving target, long-time coherent integration may effectively improve the imaging quality, whereas the imaging performance may severely degrade due to the range migration and the Doppler frequency migration. In this paper, a novel motion parameter estimation method named second-order Wigner-Ville distribution (SoWVD) transform is proposed, and then, a new SAR imaging method based on the SoWVD for a ground moving target is developed. As a modified Wigner-Ville distribution method, the SoWVD method can estimate the motion parameter without the search procedure, which achieves motion parameter estimation by Fourier transform operations in the 2-D frequency plane with respect to the slow time and the delay time. In addition, it can effectively recognize the cross terms based on multiple symmetrical properties of the peaks in the 2-D frequency domain. Both simulated and real data processing results are presented to validate the proposed imaging method.

In the conventional space-time signal model, the statistical amplitude of the clutter is assumed to be a specific distribution. For sea clutter, a pulse-to-pulse correlation matrix is added to the temporal covariance matrix to describe the motion of the sea surface. However, such sea clutter model cannot reflect the property of the sea clutter. In this paper, a space-time model for the sea clutter is presented based on the physical sea surface model, in which the clutter amplitude is deterministic instead of statistic. The reflectivity and the radial velocity of the sea clutter for any specific position and time are computed based on the physical sea surface. Both the aforementioned two factors vary with time, which corresponds to the time variation of the sea surface. Moreover, the spatial channel decorrelation is modeled, which has an effect on the spatial covariance matrix of the sea clutter. The simulated angle-Doppler spectra and the signal-to-clutter-plus-noise-ratio loss show that the reflectivity of the sea clutter makes the space-time-adaptive-processing performance degrade, and the radial velocity of the sea clutter results in a more significant spread of the clutter power spectrum in sea state 4 than sea state 2. The Doppler spectrum can be acquired by the space-time model proposed in this paper instead of the experimental Doppler model for sea clutter. For a given sea state, the low range resolution has a more prominent effect on the power spectrum because of severer spatial channel decorrelation.

A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

In practice, the ambiguous image of the strong ground clutter may dominate in the adjacent water areas in the case of high-azimuth ambiguity level for the spaceborne synthetic aperture radar. In this letter, a robust ambiguous clutter suppression method is proposed for the ground moving target indication. The subspace projection approach is used to suppress the ambiguous clutter, and the notch broadening technique is used to widen the ambiguous clutter notches for the robustness. The width of the notch is determined by the probability density function of the interferogram's phase. The validity and robustness of the proposed method are verified by the simulation results.

The scale-invariant feature transform (SIFT) algorithm has been widely applied to optical image registration. However, mostly because of multiplicative speckle noise, SIFT has a limited performance when directly applied to synthetic aperture radar (SAR) image. In this letter, a novel SAR image registration method is proposed, which is based on the combination of SIFT, nonlinear diffusion, and phase congruency. In our proposed algorithm, the multiscale representation of a SAR image is generated by nonlinear diffusion, since it better preserves edges in the image as opposed to Gaussian smoothing, which is used in the original SIFT. To reduce the influence of multiplicative speckle noise, the ratio of exponential weighted average operator is used to compute the gradient information in the construction of nonlinear diffusion scale space. Moreover, phase congruency information is utilized to remove the erroneous keypoints within the initial keypoints. Experimental results on multipolarization, multiband, and multitemporal SAR images indicate that our algorithm can improve the match performance compared to the SIFT-based method, which leads to a subpixel accuracy for all the tested image pairs.

Clutter suppression and ground moving target indication are challenging tasks in multichannel synthetic aperture radar (SAR) systems. In recent years, robust principal component analysis (RPCA) has attracted much attention for its good performance in distinguishing the different parts from a set of correlative database. Therefore, we propose a fast RPCA-based detection method for multichannel SAR under a strong clutter background in this letter even with channel unbalance or platform motion error. Subsequently, as the existing space-time adaptive processing (STAP) method would fail when the training samples are contaminated by the moving target, we apply the RPCA-based method in the range-Doppler domain to improve the performance of STAP. Since the regions of targets can be detected via RPCA, the remaining samples, which can be regarded as only clutter, are used to estimate the covariance matrix for further processing. The final experiments based on real measured data set show its good performance under the strong clutter background. Although the RPCA-based result differs from that of the STAP method, they can work cooperatively to get a more robust detection performance.

Efficient motion parameter estimation is a key challenge for moving-target imaging and localization in the synthetic aperture radar ground moving-target indication system. However, the existing methods suffer from ambiguities, complex realization, or heavy computation load of O(MN). To solve these problems, we propose an efficient Radon transform (RT) and an efficient fractional Fourier transform (FRFT) to estimate the radial velocity and azimuth velocity. By exploiting the geometry information, we model a geometry relationship between the motion parameters and two transform angles of RT or FRFT. The matched motion parameters can be estimated by the geometry relationship of the mismatched results, and the computation complexity is reduced from O(MN) to O(2N) effectively. Additionally, the symmetry property can be used for clutter canceling. Simulated and experimental results demonstrate the validity of the proposed methods. Compared with conventional motion parameter estimation methods, the proposed methods are much more efficient in acquiring accurate estimation results.

Compressed sensing (CS) has been increasingly used in the synthetic aperture radar ground moving-target indication system, particularly for imaging the moving targets, which satisfy the sparse precondition of the CS method. However, efficient moving-target imaging is a key challenge for current CS methods, since the redundant basis brings heavy computation load. In this letter, by exploiting the geometry information of the defocused results, we present an efficient fractional Fourier transform (FRFT) to estimate the Doppler rate and image the moving targets by only two times FRFT rather than time-consuming searching operation. Then, the concept is extended into an efficient CS (ECS) imaging method by two bases consisting of two discrete FRFT matrices rather than the redundant basis. Simulations and real-data process are provided to demonstrate the effectiveness of the ECS method. The proposed ECS method can achieve accurate parameter estimation and imaging performance with low computational complexity.

Short data and large Doppler bandwidth in a low-pulse-repetition-frequency system is a challenging problem for space debris imaging. Meanwhile, space debris usually rotates at a high speed so that the echo suffers from the shadow effect during the observation. To solve the problem, we propose a new 2-D inverse synthetic aperture radar imaging algorithm. Based on the fact that space debris usually rotates for several periods during the observation and the scattering field presents strong spatial sparsity, the proposed method can efficiently improve the imaging quality by constructing the measurement matrix to utilize the support data in multiple cycles. Theoretical analysis confirms that the methodology can obtain a well-focused image. Numerical simulations demonstrate the effectiveness of the proposed algorithm for space debris imaging.

In this paper, the problem of joint multi-pitch and direction-of-arrival (DOA) estimation for multichannel harmonic sinusoidal signals is considered. A spatio-temporal matrix signal model for a uniform linear array is defined, and then the ESPRIT method based on subspace techniques that exploits the invariance property in the time domain is first used to estimate the multi pitch frequencies of multiple harmonic signals. Followed by the estimated pitch frequencies, the DOA estimations based on the ESPRIT method are also presented by using the shift invariance structure in the spatial domain. Compared to the existing state-of-the-art algorithms, the proposed method based on ESPRIT without 2-D searching is computationally more efficient but performs similarly. An asymptotic performance analysis of the DOA and pitch estimation of the proposed method are also presented. Finally, the effectiveness of the proposed method is illustrated on a synthetic signal as well as real-life recorded data.

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios.

In this paper, a robust beamforming method is proposed. This method can be viewed as a LCMV beamformer with its response vector further optimized. To generate a better response vector, it is first established as a non-convex quadratically constrained quadratic programming problem, and then is transformed into a semidefinite programming problem which can be efficiently and exactly solved via semidefinite relaxation. This method outperforms the traditional LCMV beamformer with lower sidelobe and well-maintained mainbeam. Moreover, the computation complexity is negligible because the size of the response vector is relatively small. Simulation examples are carried out to demonstrate the effectiveness of the proposed method.

This study presents several new properties of the discrete Fourier transform (DFT) modulated filter banks and efficient algorithm for designing the filter banks with very large‐scale (with a very large number of subbands and very long filters). For DFT modulated filter bank, the authors derive the symmetric property of the overall transfer function and aliasing transfer functions which can be efficiently calculated by using the orthogonal property of the DFT matrix. By invoking the M th band condition and new property, an efficient algorithm is proposed to design DFT modulated filter banks. The convergence of the algorithm is also proved. Several numerical examples and comparison with the conventional methods are included to show the effectiveness of the proposed algorithm.

Accurate focusing of highly squinted azimuth-variant bistatic synthetic aperture radar data is a difficult issue due to the relatively large range migration, sensibility of the higher order terms, and the inherent geometric variance. To accommodate for this problem, extended azimuth nonlinear chirp scaling algorithm is investigated in this letter. First, range-azimuth coupling is mitigated through a linear range walk correction operation, and then, bulk secondary range compression is implemented to compensate the residual range cell migration and cross-coupling terms. Following which, the characteristics of the azimuth-dependent quadratic and cubic phase terms are analyzed, and modified scaling coefficients are derived by adopting higher order approximation and incorporating the azimuth-dependent range offset caused by the inherent geometric configuration. Compared with traditional nonlinear chirp scaling method, large azimuth depth of focusing can be realized without changing the overall procedure. Simulation results validate the effectiveness of the proposed algorithm.

Due to the characteristic of the synthetic aperture radar (SAR) image's sensitivity to the target aspect angles, a multiple sparse representation (MSR) method for SAR target configuration recognition is proposed. Making use of the prior information, dictionaries are constructed by using the samples of each configuration to better capture the detail information of the SAR images. The advantage of MSR over sparse representation for detail feature extraction is analyzed. Moreover, to achieve better recognition results, the Dempster–Shafer fusion is carried out to get comprehensive description of the target for configuration recognition. Two mass functions are constructed based on MSR and the sample statistical property. The combined mass function has the advantages of both the detail and global features of the target. Experiments on the moving and stationary target acquisition and recognition data sets validate the effectiveness and superiority of the proposed algorithm.

In practice, inevitable image coregistration error and channel phase mismatch will significantly degrade the estimation performance of the target radial velocity in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. To overcome this problem, a new radial velocity estimation method using the subspace projection (SP) algorithm with adaptive data reconstruction is proposed in this letter. Based on the joint-pixel signal model, the Wiener weight vector is used to reconstruct the multichannel data vector of the pixel containing the moving target. Then, the SP algorithm is adopted to deal with the radial velocity estimation with the reconstructed single “snapshot” data. The validity and robustness are verified by both simulations and real SAR data experiments.

High-resolution imaging for squint azimuth-variant bistatic synthetic aperture radar system is a challenging task due to the existence of the spatial variance of range cell migration (RCM) and Doppler frequency modulation (FM) rate. To address this problem, azimuth nonlinear chirp scaling (ANLCS) is investigated in this letter. First, linear range walk is removed and then ANLCS is applied in the range frequency azimuth time domain to correct the azimuth-variant RCMs and to equalize the different FM rates. Taking the 2-D variance caused by the azimuth-variant configuration into consideration, a new perturbation function is derived based on the bistatic geometry. Using method of series reversion, a close form of range-azimuth coupling is obtained and corrected in the range Doppler domain by an interpolation-free operation. Incorporated with the secondary range compression, this method leads to a more accurate focusing for azimuth-variant bistatic configurations, even with high squints. Simulation results validate the effectiveness of the method.

In this letter, we present the mode of forward-moving rotor synthetic aperture radar (ROSAR) to achieve 3-D imaging for the front area of the low-altitude aircraft. First, the geometric model is given, and the signal property is analyzed. Based on which, spatial offsets and coupling in the azimuth and along-track directions are corrected in the frequency domain, and then, the forward-moving ROSAR is simplified as a “stop-and-go” mode in the along-track direction. After 2-D imaging in the range-azimuth direction, a 3-D image can be obtained by range-along-track focusing with the improved range-Doppler algorithm. Moreover, azimuth depth of focusing is also given to maintain the imaging quality. Finally, simulation results prove the feasibility and effectiveness of the proposed method for this new imaging mode.

High-resolution synthetic aperture radar (SAR) imagery of a wide area of surveillance is a difficult large-data problem. In the past few years, researchers have applied compressive sensing (CS) to SAR, as it exploits redundancy in signals. To further extend the sparse problem from the vector to the matrix, a new theory called matrix completion (MC) has attracted much attention, which can complete a matrix from a small set of corrupted entries based on the assumption that the matrix is essentially of low rank. Inspired by this technique, a novel SAR imaging algorithm is proposed in this letter to deal with the undersampled data. After representing the data of a range cell as a matrix, the phase is compensated to keep the matrix holding the property of low rank. Subsequently, MC can be utilized to recover the full-aperture data in the new constructed matrix. Since the data are completely unsampled in the corresponding azimuth cells, the proposed method has effectively conquered the restriction of previous applications that each received channel must have a small number of samples. The final results in both simulation and real-data experiments show that the targets can be well focused even in the scenario of discarding a large percentage of the received pulses. Moreover, when compared with CS, the method is not required to design the complicated measurement matrix.

Weak-target detection and imaging are the challenging problems of airborne or spaceborne early warning radar. The envelope of a high-speed weak target after range compression spreads over range during the long observation period. To finely refocus a high-speed weak maneuvering target, motion parameters should be accurately obtained for compensating the envelope. This letter proposes a new imaging approach for high-speed maneuvering targets without a priori knowledge of their motion parameters. In this method, the azimuth compression function is constructed in a range and azimuth 2-D frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets. Simulation results show that the proposed algorithm improves the performance for detecting and imaging high-speed maneuvering targets.

Using multichannel in azimuth to suppress the Doppler ambiguities allows for high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) imaging, thus overcoming the minimum antenna area constraint of the conventional space-borne SAR. If the degrees of freedom in azimuth are used for the clutter suppression, the ground moving target indication (GMTI) can be achieved. Therefore, a space-borne multichannel SAR system for GMTI has the potential to offer HRWS imaging capability to some extent. However, here, the GMTI performance may suffer from the Doppler ambiguity caused by the undersampling. This paper focuses on the analysis of the influence of pulse repetition frequency (PRF) on GMTI performance in Doppler ambiguity. The multichannel signal models of the clutter and the moving target with Doppler ambiguity are derived in complex image domain. Considering the Doppler ambiguity and the multichannel spatial ambiguity, the influence of PRF on the output signal-to-clutter-plus-noise ratio after adaptive clutter suppression and the signal-to-noise ratio loss after SAR imaging is discussed. Accordingly, the design considerations of PRF for optimizing GMTI performance in multichannel SAR systems with HRWS imaging capability are given, along with a simulation example. Finally, the real-data experiments verify the theoretical investigations.

The triplet Markov fields (TMF) model has been successfully applied to statistical segmentation of nonstationary images by introducing the auxiliary field, which represents the different stationarities of images. Commonly, the TMF adopts a four-nearest neighborhood. This limits the modeling ability for complex priors. Therefore, this paper suggests using a higher order neighborhood-based TMF (HN-TMF). In the HN-TMF, the autocovariance analysis is applied to reveal the local fluctuation at each site. The auxiliary field is then redefined based on the local fluctuation information to denote homogeneity or heterogeneity. Based on the auxiliary field, the local energy function in HN-TMF is constructed either in a homogeneous or heterogeneous way, and hence, the local structure can be embedded in the energy function to improve the prior modeling ability. Along with the newly constructed energy function, new initializations of HN-TMF parameters are given to fulfill the physical interpretation of the energy function. The experiments performed on both synthetic and real synthetic aperture radar images demonstrate the effectiveness of the proposed HN-TMF in both speckle noise reduction and heterogeneous region segmentation accuracy.

Under the condition of time variant RFI, the limitation of training sample size causes great performance degradation of the conventional Radio Frequency Interference (RFI) suppression algorithm based on eigen-subspace projection (ESP) method. A novel RFI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method estimates the RFI power spectrum adaptively and iteratively, utilizing few training samples and filtering the RFI based on the OSP method. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.

We focus on the problem of detecting a signal in compound-Gaussian clutter, where the texture is a random variable with Gamma or inverse Gamma distribution. The persymmetric structure of the covariance matrix is exploited and a persymmetric generalized likelihood ratio test (Per-GLRT) using a three-step procedure is proposed. In addition, we prove that the Per-GLRT ensures constant false alarm rate (CFAR) property with respect to the covariance matrix. Finally, the detector is assessed by Monte Carlo simulations. Performance comparison of the Per-GLRT with the traditional GLRT shows that the former improves the detection performance in training-limited scenarios.

In this paper, we consider the direction of arrival (DOA) estimation problem under the coexistence of noncircular and circular signals. By exploiting the difference between the circularity of noncircular and circular signals, a method is proposed, which estimates the DOAs of noncircular and circular signals separately. The maximum number of detectable directions by the proposed method is twice that by the MUSIC method. Furthermore, since the proposed method resolves noncircular and circular signals based on the circularity difference rather than the DOA difference, the proposed method performs well regardless of the DOA separation between noncircular and circular signals. Simulation results illustrate the effectiveness of the proposed method.

In this letter, we consider the problem of estimating gain-phase and position errors for constellation synthetic aperture radar (SAR) systems. In the conventional method, the position error estimation is based on the first-order Taylor series expansion of the position-error exponential function. However, the first-order Taylor series expansion causes an approximation error, resulting in the inaccuracy of the estimation by the conventional method. In this letter, an improved method is developed to overcome this problem, based on the fact that the aforementioned approximation error decreases with the reduction in position errors. In the improved method, we first compensate the position error estimates obtained at thekth iteration in order to reduce the remaining position errors at the (k+ 1) th iteration. Then, the position error estimates obtained at all iterations are summed as the estimates of the true position errors. In this way, the improved method removes the aforementioned approximation error, leading to estimates with high accuracy. Simulation results verify that the estimates by the improved method are closer to the true array errors than those by the conventional method. In addition, simulation results show that the improved method is more robust to position errors than the conventional method. Furthermore, the increase in the computational load of the improved method is negligible.

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data.

In practice, unavoidable array errors, consisting of phase and position errors, significantly degrade the performance of constellation synthetic aperture radar (SAR) systems. Therefore, methods are required to estimate these errors. In constellation SAR systems, the clutter spectrum components within a Doppler bin can be used as calibration sources with known directions. In this letter, it is observed that the steering vectors of the spectrum components in one Doppler bin are conjugate with those of the spectrum components in its contrary Doppler bin on condition that each SAR operates in side-looking mode and its nominal left coordinate is zero, which is easy to realize. We obtain a new phase error estimation method based on this observation. An array error estimation method, which estimates phase and position errors simultaneously, is proposed via combining the new phase error estimation method with the least squares method for estimating position errors. The advantages of the proposed method include its capability to directly estimate phase and position errors without joint iteration between the estimations of phase and position errors; thus, it performs well, while the conventional method behaves unstably because it may converge to a local optimal solution, when position errors are large. Furthermore, mathematical analysis indicates that the proposed method has less computational load. In addition, computer simulations show that it performs better than the conventional method. The only cost is that it employs twice as many Doppler bins as the conventional method does, which is endurable because there are numerous Doppler bins.

In the above titled paper (ibid., vol. 7, no. 4, pp. 731-735, Oct. 10), there are errors in Section IV which are corrected here.

In this paper, we propose a method based on minimum mean squared error (mmse) criterion to estimate synthetic aperture radar interferometry (InSAR) interferometric phase. In this method, the cross-correlation coefficient vector with large coregistration error is given first, and then, the cost function under the mmse criterion is used to estimate the InSAR interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide an accurate estimate of the terrain interferometric phase (interferogram) even when the coregistration error reaches 1 pixel.

This paper describes an ambiguity resolving approach for slant-range velocity estimation which utilizes the wideband characteristic of the transmitted signal (multiple wavelengths). Based on the wavelength dual-wavelength radar data. Then, two effective approaches are introduced to focus the moving target no matter the Doppler ambiguity exists or not. The slant-range velocity is estimated by the number of azimuth cell displacements between the two focused images. Both imaging methods have different properties and advantages. A performance analysis is made, and deleterious factors in practice are analyzed in detail. The effectiveness of the unambiguous slant-range velocity estimation approach is demonstrated with the use of simulated and real data.

Phase unwrapping is the key problem in building the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (SAR) system data. In this paper, we propose a method of phase unwrapping based on the model of the multibaseline joint data group. The method can not only adaptively coregister the SAR images, but also accurately provide the accurate estimation of the terrain unwrapped phase in the presence of the large coregistration errors. Moreover, the improvement in computational complexity is achieved by using the multibaseline joint data group. The method is investigated by simulations, and results show successful phase unwrapping even if the image coregistration error is close to one pixel.

Space-time adaptive processing (STAP) is an effective tool for moving target detection. Conventional STAP methodologies process the angular and Doppler two dimensional data vector. In practical applications, adjacent range cells are statistically dependent due to filtering, since the point spreading function of a target is not an ideal delta function. In this paper, a novel approach incorporating range (fast time) information in STAP is presented for clutter rejection, which we term space-time-range adaptive processing (STRAP). This method takes advantage of the correlation information of neighboring range cells. Therefore, the stationary clutter can be suppressed better compared with traditional STAP algorithms ignoring fast time information, resulting in more effective moving target detection. The validity of the STRAP algorithm is verified by the experiments of processing the real measured data of the three-channel X-band radar and MCARM radar systems.

Phase unwrapping is a key problem not only in all quantitative applications of synthetic aperture radar (SAR) interferometry but also in other fields. In this letter, a new phase unwrapping approach is investigated. Our study is based on the model of the optimum data vector. In order to autocoregister the SAR images, the proposed method takes advantage of the multibaseline optimal weighted joint data vector by extracting all the coherence information available in the neighboring pixels. Moreover, the method employs the projection of the joint signal subspace onto the corresponding noise subspace to estimate the unwrapped interferometric phases (or the terrain heights). The proposed method can accurately determine the dimensions of the noise subspace and provide the robust unwrapped interferometric phases even in the presence of the large image coregistration errors. Moreover, the multibaseline processing idea is a combination of data optimization, image coregistration, interferogram filtering, and phase unwrapping.

With multisatellite radar systems, several additional features are achieved: multistatic observation, interferometry, ground moving target indication (GMTI). In this letter, a new reduced-dimensional method based on joint pixels sum–difference$(\Sigma {-} \Delta)$data for clutter rejection and GMTI is proposed. The reduced-dimensional joint pixels$ \Sigma {-} \Delta$data are obtained by the orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multisatellite radar system. In the sense of statistic expectation, the joint pixels$\Sigma {-} \Delta$data contain the common and different information among SAR images. Then, the objective of clutter cancellation and GMTI can be achieved by adaptive processing. Simulation results demonstrate the effectiveness and robustness of the proposed method even with clutter fluctuation and image coregistration errors.

In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites.

Proposed herein is a pre-processing technique applicable to maximum-SINR beamformers for "blind" space-time Rake receivers in "single-user"-type DS-CDMA detection. This smart-antennas signal-processing technique enhances the constructive summation of multipaths and the rejection of co-channel interference, both of which decrease the bit-error-rate and lower the error-floor of the cellular uplink's near-far problem at high SNR. This technique is "blind" in that it needs neither prior knowledge nor explicit estimation of (1) the channel-faded multipaths' arrival angles, relative arrival delays or power profiles, (2) the receiving antenna array's nominal or actual gain-phase-polarization response, and (3) the co-channel multi-access users' signature-spreading codes. Monte Carlo simulations show that the proposed scheme can enhance the output-SINR by 1 - 10 dB. A mathematical analysis shows this proposed pre-processing scheme to always improve the asymptotic output-SINR. A formula is analytically derived to predict this asymptotic output-SINR. This formula lies within 1 dB of limited simulation results at an input-SIR above -7 dB and a chip-rate space time-sampling, but the formula is less accurate otherwise

A new algorithm is developed to merge a high-resolution panchromatic image and a low-resolution multispectral image based on the combination of multiresolution wavelet decomposition, evolutionary strategy and the IHS transform. The high-resolution panchromatic image is firstly decomposed to the wavelet planes, then the regions are partitioned by evolutionary strategy in terms of difference of edge information from wavelet planes and the merging algorithm is done by adding edge influence factor in different region. The proposed method is compared with the IHS and the MWT methods. The results of the comparison show the proposed merger performing the best in combining and preserving spectral-spatial information for the test images.

Multiple access interference (MAI) is a key problem in DS-CDMA. Both the multiuser detection method and antenna array technique have been introduced to reduce the effect of MAI respectively. In this paper, combining wavelet networks with a space-time filter, we propose a novel multiuser detector (MUD). The complexity of the MUD only depends on that of the wavelet networks. With numerical simulations and performance analysis, it is shown that the MUD precedes the conventional Rake receiver and the space-time matched filter in eliminating MAI and near-far resistance.

Bayesian detection for asynchronous DS-CDMA systems with unknown interference and multipath fading is studied. A novel nonlinear group-blind multiuser detector is proposed, in which a Gibbs sampler is employed to perform the Bayesian multiuser detection according to the linear group-blind decorrelator output. It has the advantages of low complexity, high performance and wide applications. Simulation results are presented to demonstrate its effectiveness. Furthermore, in a coded system, the proposed detector is well suited for the turbo multiuser detection that is capable of finding the Bayesian solution without knowledge of prior distribution.

Gibbs sampler, a typical Markov chain Monte Carlo (MCMC) method that approximately solves the Bayesian problem by simple numerical computation in a completely different paradigm was previously employed for the Bayesian detection in synchronous CDMA system. To perform the Bayesian detection for the asynchronous uplink CDMA system with unknown multiuser interference and multipath fading, the Gibbs sampler combined with the linear group-blind decorrelator is proposed as a novel Bayesian multiuser detection technique in this paper. Furthermore, in the study of the effect of channel estimation error on the performance of the proposed detector, we also present an iterative Bayesian multiuser detector where more accurate parameter estimation is achieved to improve the detection performance. Simulation results verify the effectiveness of the proposed detectors. The proposed detection techniques have the advantages of low complexity, high performance and wide applications.

A novel approach called spatial-temporal correlator (STC) for code acquisition and beamforming weight acquisition in DS-CDMA mobile communication systems with adaptive antenna array is proposed in this paper. The STC uses the pilot symbol (channel) and incorporated Wiener filter with the conventional PN code correlator. The proposed STC exploits the spatial and temporal information of the received signal and thus achieve improved detection performance. The detection probability and false alarm probability of the STC is theoretically analyzed under spatial-temporal AWGN and Rayleigh fading environment. Simulation results are presented to verify the performance analysis. These results show that the proposed STC can effectively acquire the adaptive beamforming weight and have improved PN code acquisition performance.

We devise a new subspace method for estimating the differential time delay of a signal received at two separated sensors as well as the frequencies of the source signal, assuming that it consists of multiple sinusoids. The time delay and frequency estimates are related to the eigenvalues and eigenvectors of a matrix obtained from the covariances of the received signals. The effectiveness of the proposed algorithm is demonstrated via computer simulations using sinusoidal signals as well as real speech data.

A novel "self-decorrelating" technique is proposed to enhance the multipath constructive summation capability and the interference rejection capability of a class of maximum-SINR blind space-time CDMA RAKE receivers designed to tackle the near-far problem. This proposed "self-decorrelating" technique appears to have effectively removed the near-far problem's error floor at high SNR; and the proposed technique can also significant decrease the error rate. Moreover, this "blind" space-time processing receiver architecture needs no prior knowledge nor explicit estimation of (1) the channel's multipath arrival angle or arrival delay or power profile, (2) the receiver's nominal or actual antenna array manifold, and (3) the other CDMA users' signature spreading codes. Preliminary simulations suggest very significant performance improvement realizable from the proposed technique. Though developed for direct-sequence CDMA, the proposed technique might be adopted for frequency-hopping CDMA.

A learning algorithm for the principal component analysis is developed based on the least-square minimization. The dual learning rate parameters are adjusted adaptively to make the proposed algorithm capable of fast convergence and high accuracy for extracting all principal components. The proposed algorithm is robust to the error accumulation existing in the sequential principal component analysis (PCA) algorithm. We show that all information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The updating of the normalized weight vector can be referred to as a leaky Hebb's rule. The convergence of the proposed algorithm is briefly analyzed. We also establish the relation between Oja's rule and the least squares learning rule. Finally, the simulation results are given to illustrate the effectiveness of this algorithm for PCA and tracking time-varying directions-of-arrival.

We carry out a sensitivity analysis of fourth-order cumulant-based MUSIC method (FOC-MUSIC) via a generalized approach using a particular Taylor series expansion of a null spectrum function. We provide the variance of the estimate error for FOC-MUSIC with an emphasis on comparing robustness of FOC-MUSIC with that of the standard covariance matrix based MUSIC in the presence of sensor errors. We address the condition of effective aperture extension by higher-order cumulant-based processing, and give the new interpretation of effective aperture extension. A new fourth-order cumulants matrix is also given.