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 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.

The existence of range ambiguity and range dependence will seriously deteriorate the performance of space-time adaptive processing (STAP). In this regard, an adaptive range-ambiguous clutter separation method suitable for the element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radar is developed in this letter. By introducing the EPC factor in both transmit elements and pulses, the clutter located in different range-ambiguous regions can be distinguished in the transmit spatial frequency dimension. Particularly, to ensure the separated performance of range-ambiguous clutter, the EPC factor is designed. Moreover, an approach on the basis of reweighted atomic norm minimization (RANM) is developed to separate the range-ambiguous clutter, leveraging the transmit spatial frequencies of clutter located in various range ambiguity areas. Furthermore, after clutter separation, the clutter is canceled via STAP individually in each range ambiguous region. A series of simulation results validate the efficacy of the proposed approach.

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.

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.

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.

Compared with a monostatic inverse synthetic aperture radar (ISAR) imaging system, a bistatic ISAR (Bi-ISAR) system offers more comprehensive target information, along with higher system security and resistance to interference. However, the inherent geometric configuration of Bi-ISAR will introduce the linear-geometry distortion (LGD), causing the target to appear sheared in shape and impacting subsequent target recognition. To deal with this issue, in this paper, a novel deep learning-based algorithm is proposed to realize the LGD correction. In the proposed algorithm, a neural network called DoubleUNet is employed for the accurate semantic segmentation of target's ISAR image. Then the initial target ISAR image is transformed into two-dimensional point clouds based on the estimated radar parameters. Finally, the linear coupling relationship between azimuth and range dimensions is removed, beneficial to the target ISAR shape restoration and subsequent recognition. Simulation experiments validate the proposed algorithm.

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.

With the rapid development of remote sensing (RS) technology, high-resolution RS image change detection (CD) has been widely used in many applications. Pixel-based CD techniques are maneuverable and widely used, but vulnerable to noise interference. Object-based CD techniques can effectively utilize the abundant spectrum, texture, shape, and spatial information but easy-to-ignore details of RS images. How to combine the advantages of pixel-based methods and object-based methods remains a challenging problem. Besides, although supervised methods have the capability to learn from data, the true labels representing changed information of RS images are often hard to obtain. To address these issues, this article proposes a novel semisupervised CD framework for high-resolution RS images, which employs small amounts of true labeled data and a lot of unlabeled data to train the CD network. A bihierarchical feature aggregation and extraction network (BFAEN) is designed to achieve the pixelwise together with objectwise feature concatenation feature representation for the comprehensive utilization of the two-level features. In order to alleviate the coarseness and insufficiency of labeled samples, a confident learning algorithm is used to eliminate noisy labels and a novel loss function is designed for training the model using true- and pseudo-labels in a semisupervised fashion. Experimental results on real datasets demonstrate the effectiveness and superiority of the proposed method.

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.

In a direction-finding process, high-resolution subspace-based algorithms are the most popular ones. It is well-known that their performance of direction of arrival estimation mainly depends on the accuracy of the signal subspace. However, the traditional methods of capturing the signal subspace do not mine the information hidden in the array output in depth, which may restrict their application to some extent. In this study, we elaborate on a novel scheme to extract the signal subspace through refinement of the correlation matrix of the array output. In the developed scheme, a collection of spatial temporal correlation matrices is firstly established. Then, we define a weighting vector for the correlation matrices, and take the weighted average of the correlation matrices as the covariance matrix of the array output. It is clear that this covariance matrix is more general than the traditional covariance matrix, and the signal subspace can be optimized through adjustment of the weighting vector. In this study, we present an optimal weighting vector by adopting the particle swarm optimization. Simulation results demonstrate that the proposed approach has better performance in root mean square error compared to the existing schemes.

This letter focuses on discriminating true target from mainlobe jammers using a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. To this end, a three-stage detection and discrimination architecture is proposed. Specifically, mainlobe jammers caused by large time delays can be discriminated from the true target based on the range degrees-of-freedom (DOFs) of FDA-MIMO radar, while the true target and jammers generated with small time delays are distinguishable in the fast time domain. In particular, a binary hypothesis test is formulated for discrimination, where the$H_{1}$hypothesis contains the true target, while the$H_{0}$hypothesis contains the unknown mainlobe jammer. The mainlobe jammer can be formulated as either a known subspace model or an unknown rank-one model. The former leads to a subspace-based selective detector (SSD) and the latter generates a rank-one-based SD (ROSD). Then, the formulated tests are handled using the generalized likelihood ratio test (GLRT) criterion. At the analysis stage, the selectivity and detection performance are compared with the conventional GLRT and selective receivers. Simulation results validate that the proposed detectors achieve satisfactory detection performance while providing the selectivity to mainlobe jammers.

In this letter, the problem of adaptive target detection for a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar in the presence of unknown mutual coupling (MC) is investigated. Unlike the conventional phased array (PA) radar, the signal model of FDA-MIMO radar in unknown MC is constructed using two symmetric Toeplitz matrices accounting for the effects of unknown MC at the transmitter and receiver. At the formulation stage, the detection problem is formulated as either a binary hypothesis test or a multi-hypothesis test. The former is tackled based on the generalized likelihood ratio test (GLRT) leading to a robust detector (RD). The latter resorts to the penalized test and multifamily LRT (MFLRT), resulting to Modified-Multiple Hypothesis Penalized Likelihood Ratio Test (M-MHPLRT) and MFLRT detectors, respectively. At the analysis stage, the effectiveness of the developed detectors is verified with comparisons among the benchmarks and existing detectors via Monte Carlo simulation results

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.

Forward-looking mode for moving target detection is important for airborne radar systems, however, it is difficult to suppress the range-dependent clutter in the presence of range ambiguity using the traditional space-time adaptive processing (STAP) techniques. In this paper, a vertical coherent frequency diverse array (FDA) radar using quadratic phase coding (QPC) is proposed to alleviate the range-ambiguous clutter problem. The proposed vertical coherent FDA radar has two major advantages: i) it uses an identical baseband waveform for each transmit element, which prevents the unreal orthogonal waveform assumption in multiple-input multiple-output (MIMO) framework; ii) it achieves wide spatial coverage in elevation within a single pulse duration, which is desirable for the airborne reconnaissance radar. The space-frequency coupled characteristic of vertical coherent FDA is revealed and a series of 2-dimensional (2-D) matched filters are designed, which is helpful for range-ambiguous clutter separation. Based on the QPC technique, the residual clutter is suppressed by orthogonal projection (OP) filtering, which further improves the target detection performance. With the proposed vertical coherent FDA using QPC, the range-ambiguous clutter can be separated successfully, and the clutter suppression performance is improved. Simulation results are presented to verify the effectiveness of the proposed method in serious range-ambiguous clutter scenarios.

In this paper, two novel methods for SAR image super-resolution (SR) are proposed. The main challenge for SAR image SR reconstruction is speckle noise. For this reason, a novel algorithm termed the importance sampling cubature Kalman filter (ISCKF) is proposed to reconstruct a high resolution (HR) image from a series of low-resolution (LR) images. However, as the reconstructed image usually embraces residual noise visually, we establish a nonlinear low-rank optimization model in order to further reduce the speckle noise drastically. Correspondingly, an alternating direction method of multipliers based on the low-rank model (ADMM-LR) algorithm is proposed to solve it, which yields the other novel method termed ISCKF+ADMM-LR for SAR image SR. In addition, we establish the computational complexity of the proposed algorithms. The experimental results of both the simulated images and real SAR images demonstrate that the performance of the proposed methods are superior to some state-of-art methods in both SR and despeckle.

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.

As a new imaging framework, the rotating synthetic aperture radar (ROSAR) derives a synthetic aperture through antenna rotation instead of traditional linear platform motion. The rotational synthetic aperture is sensitive to high-frequency vibrations aboard helicopters. The vibration causes severe phase error in signal echoes and imaging degradation. Unlike traditional synthetic aperture radar (SAR) imaging where range-Doppler algorithm (RDA) maybe applied for autofocus to improve the imaging performance, vibration phase errors cannot be estimated via conventional imaging algorithms due to severe range and azimuth couplings. A new ROSAR imaging procedure is proposed to compensate the phase error resulting from high-frequency vibrations of helicopters. A vibration model of ROSAR signal echo is established. The double Doppler keystone transform (DDKT) is adopted to correct the range cell migration (RCM) induced by slant range history and vibration errors. Analytical echo expression with range-independent vibration phase error is also derived in greater details. The focused image can then be obtained via classical autofocus algorithms. The effectiveness of the proposed technique is sufficiently demonstrated through simulation studies.

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.

Recently, deep-learning-based feature extraction (FE) methods have shown great potential in hyperspectral image (HSI) processing. Unfortunately, it also brings a challenge that the training of the deep learning networks always requires large amounts of labeled samples, which is hardly available for HSI data. To address this issue, in this article, a novel unsupervised deep-learning-based FE method is proposed, which is trained in an end-to-end style. The proposed framework consists of an encoder subnetwork and a decoder subnetwork. The structure of the two subnetworks is symmetric for obtaining better downsampling and upsampling representation. Considering both spectral and spatial information, 3-D all convolution nets and deconvolution nets are used to structure the encoder subnetwork and decoder subnetwork, respectively. However, 3-D convolution and deconvolution kernels bring more parameters, which can deteriorate the quality of the obtained features. To alleviate this problem, a novel cost function with a sparse regular term is designed to obtain more robust feature representation. Experimental results on publicly available datasets indicate that the proposed method can obtain robust and effective features for subsequent classification tasks.

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.

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.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

In microwave staring imaging, spatial-resolution of real aperture imaging is limited by actual antenna array aperture. In order to achieve high-resolution imaging of targets with sparse feature, this paper proposes a high-resolution imaging method based on temporal-spatial stochastic radiation field combining compressive sensing (CS) theory. Firstly, the formation and property of temporal-spatial stochastic radiation field are discussed. Then, signal model based on random radiation field is deduced in detail, and on this basis, high-resolution imaging method based on CS is discussed. The proposed method can distinguish targets within the beam coverage and higher quality image is achieved. Finally, numerical simulations and experiments in microwave chamber are performed to validate the method and its analysis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.