High mobility of space-based radar (SBR) platforms risks target velocities falling below the minimum detectable velocity (MDV), rendering them undetectable in main-lobe clutter. Aiming at multi-target tracking (MTT) in space-based multiple-input multiple-output (MIMO) radar systems, this paper proposes a joint beam and dwell time allocation (JBTA) strategy. This strategy incorporates the MDV constraint and adopts the Bayesian Cramér-Rao Lower Bound (BCRLB) as the performance metric, where BCRLB is a lower bound for the mean square error (MSE) of target state estimation. To solve the non-convex mixed-integer optimization problem of JBTA, a two-step decomposition approach is designed. Numerical results verify that JBTA effectively improves global MTT performance.

Dynamic anomaly detection of space targets is essential for space situational awareness. With the intrinsic Range Doppler imaging mechanism, the spatio-temporal feature of ISAR sequences has a great potential in dynamic representation. To address this, we propose a novel dynamic anomaly detection method using Spatio-Temporal Graph Convolutional Networks (STGCN) to capture spatio-temporal features from inverse synthetic aperture radar (ISAR) image sequences. By constructing a skeleton model according to the universal geometry of space satellites, our method captures node correlations in both temporal and spatial dimensions from continuous ISAR frames, thereby enabling reliable dynamic target recognition. The effectiveness and superiority of this approach are demonstrated through comparative experiments.

By unfolding traditional optimization algorithms into the form of neural networks, the unfolding network methods have attracted more and more attention in sparse inverse synthetic aperture radar (ISAR) imaging because of their high reconstruction performance and good interpretability. However, existing unfolding network methods mainly focus on 1-D sparse ISAR imaging and cannot be directly applied to 2-D sparse ISAR data. For this reason, a novel learned 2D-two-step iterative shrinkage/thresholding algorithm (L-2D-TwISTA) is proposed for high-efficiency and high-accuracy 2-D sparse ISAR imaging. Specifically, each stage of L-2D-TwISTA corresponds to an iterative solution step of the developed 2D-TwISTA approach. Moreover, a complex-valued (CV) residual network is designed in L-2D-TwISTA to improve training efficiency and solve the nonlinear problem of proximal mapping of the 2D-TwISTA more effectively. The experimental results of real-measured data confirm that the L-2D-TwISTA can realize high-performance 2-D sparse ISAR imaging.

In high-resolution unmanned aerial vehicle (UAV) Synthetic Aperture Radar (SAR) imaging, despite the utilization of inertial navigation system (INS) data for pre-compensation, residual envelope and phase errors may still persist. Residual envelope errors can adversely affect phase error estimation (PEE), consequently degrading autofocus performance. This letter proposes a phase-envelope joint autofocus algorithm for backprojection (BP) imaging. An algorithm framework for alternating iterative estimation of phase and envelope is constructed based on maximizing image sharpness, which can effectively correct phase and envelope errors. In addition, only a small local area is selected for error estimation, significantly reducing memory and time consumption. The effectiveness of the algorithm is verified using X-band UAV SAR raw data.

An efficient raw data generation (RDG) algorithm in the hybrid domain is proposed, which can be applied to the accurate echo generation of spotlight mode synthetic aperture radar (SAR) with trajectory deviation, even in cases of terrain undulation. Generally, the accuracy required for the signal’s envelope is on the order of the range resolution cell. However, the requirement for the phase is significantly higher, on the order of the wavelength. Therefore, the proposed algorithm calculates the phase of the SAR raw data in the time domain through a point-by-point approach to ensure accuracy and computes the envelope of the raw data through subblock processing in the frequency domain to improve efficiency. To balance the computational efficiency and accuracy, the optimal selection of subblock size is discussed in detail. Simulation experiments verify the accuracy and efficiency of the method.

Due to the complexity of the range model and the severe range-azimuth coupling in the signal echoes during the diving flight of missile-borne synthetic aperture radar (SAR), the traditional frequency-domain algorithms have the limitation of accuracy in the processing of missile-borne SAR imaging, and the complexity of the algorithm is relatively high. To solve the problem of mismatch between the algorithm and the range model in the diving state, an improved parametric polar format algorithm (PPFA) based on equivalent range model is proposed. First, this letter transforms the diving trajectory model of the missile-borne into an equivalent range model applicable to horizontal straight flight. Then, based on the equivalent range model, and considering the spatial variability of the equivalent velocity and squint angle, we improve the azimuth-focusing operation of PPFA. These enhancements resolve the issue of poor imaging effect of edge points by using traditional PPFA, significantly improving the edge point focusing performance. The effectiveness and feasibility of the proposed algorithm are verified by the experimental simulation results and various indexes.

In radar systems, high-resolution waveforms with favorable correlation properties are preferred. This letter addresses the challenge of designing unimodular radar waveform sets with low peak sidelobe level (PSL) in autocorrelation function (ACF). In contrast to conventional methods, this approach does not attempt to transform a non-convex problem into a convex one through relaxation. Inspired by neural network optimization techniques, an iterative neural network structure for minimizing PSL is proposed in this letter. By employing the Mellowmax operation and incorporating an additional penalty term into the loss function, the optimized ACF with low PSL is obtained. Corresponding simulation experiments demonstrate that our method achieves a superior PSL value of 2-3 dB lower than the state-of-the-art method.

The high speed and maneuverability of the spacecraft make the traditional wideband tracking algorithms fail to achieve echo coherent recovery under low SNR, resulting in reduced tracking accuracy. This paper proposes a fully coherent accurate measurement and tracking method based on the multi-channel hypothesis to realize the fully coherent recovery of dechirp echo and Doppler phase disambiguation. The algorithm performs initialization estimation and designs the multi-channel hypothesis velocity ambiguity interval. Then, it traverses the interval to perform the fully coherent accurate measurement and tracking to obtain the motion state update, employed to echo coherent recovery. Finally, the N/M criterion is introduced to evaluate the envelope alignment of the recovered echoes and filter out the unambiguous velocity. Experiments demonstrate its remarkable performance in low SNR conditions.

Azimuth multichannel synthetic aperture radar (SAR) possesses high-resolution and wide-swath (HRWS) imaging capabilities, posing a significant threat to the survivability of critical targets during wartime. However, due to the constraints of multichannel reconstruction processing, existing jamming technology will produce a periodic jamming effect in the azimuth direction when against HRWS-SAR. This unique periodic repetition related to the number of channels will make the jamming easy to detect and eliminate. Meanwhile, the amplitude of the jamming signal suffers from the modulation of sine functions, which causes a waste of jamming energy. Against the above problems, this article introduces a presegmentation idea, effectively circumventing the aforementioned problems. Meanwhile, this article proposes a 2-D precise controllable smart jamming method designed for HRWS-SAR. The proposed method can generate two jamming effects, barrage and deceptive, at designated locations in the targeted area. First, 2-D expand factors are defined, respectively, to achieve precise control of the barrage jamming range. Then, by controlling multiple jamming parameters, a highly controllable deceptive jamming effect can be achieved. Additionally, a parameterized jamming model is established, which can achieve 2-D precise control of the jamming position through the cooperation of the signal and spatial domain. Notably, this study focuses not only on the impact of different jamming parameters on imaging results but also on evaluating jamming effectiveness under parameter estimation errors to guide optimal jamming strategies. Finally, extensive numerical simulation experiments and comparisons with other methods are conducted to verify the superiority of the proposed method.

Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is an important technical means for achieving forward-looking imaging and enhancing the visibility of the traditional SAR forward blind zone. Its significance extends across various applications, including military reconnaissance, geological exploration, disaster monitoring, and other related fields. However, motion errors, slant range errors, and other factors introduce Doppler errors, making FLMC-SAR imaging and Doppler ambiguity resolving a challenging task. In this paper, leveraging the physical characteristics that the maximum Doppler frequency within the imaging area is provided by targets in the direction of the radar platform’s velocity, we achieve pixel-level Doppler centroid correction. This holds significant implications for Doppler ambiguity resolving, distortion correction, and pixel localization in FLMC-SAR images. Initially, we established the space-time model of FLMC-SAR. Based on this model, we devised a two-step FLMC-SAR imaging processing: time-domain imaging followed by spatial-domain Doppler ambiguity resolving. Within this framework, we analyze the space-time characteristic representation of the maximum Doppler line in the two-dimensional range-Doppler image domain, and the estimation of Doppler error was provided by space-time characteristic. Furthermore, utilizing series inversion, we derived estimations of motion error and slant range error, thereby achieving pixel-level Doppler centroid frequency correction. Extensive simulations and real-data experiments demonstrate that the proposed algorithm is capable of FLMC-SAR imaging for Doppler ambiguity resolving and distortion correction.

The existing Ground Cartesian Back-Projection (GCBP) algorithm is limited by its low effectiveness in spectral compression, which makes it unsuitable for processing high-squint and large-scale strip-map Synthetic Aperture Radar (SAR) data. To address this issue, we propose a novel algorithm called Ground Squint Fast Back Projection (GSFBP) for high-squint strip-map SAR imaging. First, we introduce a Ground Squint Coordinate (GSC) system that replaces the conventional Ground Cartesian Coordinate (GCC) system. The unique geometry of the GSC allows for precise rotation of the two-dimensional wavenumber spectrum without the need for auxiliary operations, significantly easing the constraints related to scene size.Moreover, the GSC framework facilitates seamless sub-image fusion through translation, eliminating the need for interpolation. Building on the original two-step spectral compression method, we have developed a GSC-specific relative spectrum inclination correction function to enhance spectral compression effectiveness. These innovations enable GSFBP to effectively manage large-scale scenes in high-squint SAR imaging. Experimental validation, using both simulated and real measured SAR data, confirms the superiority of the proposed algorithm.

Optimal hyper-parameter selection for low-rank and sparse matrix decomposition (LRSMD) in synthetic aperture radar jamming suppression is usually challenging. This letter proposes an effective approach to LRSMD jamming suppression by exploiting the polarized similarity. A polarimetric ratio function is established to straightforwardly determine the rank hyper-parameter. The polarimetric ratio function is defined as the energy ratio of the decomposed low-rank jamming component to the sparse target signal, which is consistent across multiple polarized channels due to the equal jamming gain distinguished from the target polarized scattering. The algorithm optimally exploits the polarized similarity of jamming components to determine the rank hyper-parameter. It provides enhanced robustness and accuracy in SAR jamming removal, confirmed by synthetic experiments.

Range migration (RM) and Doppler frequency migration (DFM) make it challenging to achieve coherent integration for highly maneuvering targets. Generalized Radon-Fourier transform (GRFT) can compensate for RM and DFM, enabling coherent integration for targets, but its computational cost is significant. To address the aforementioned issue, this letter proposes a novel coherent integration method based on neural networks and GRFT. The proposed method utilizes a neural network to infer target trajectories from radar echoes. It narrows down the motion parameter search range of the GRFT method based on the inferred target trajectories, completing the coherent integration of target energy. Compared to GRFT and other existing methods, the proposed method achieves a better balance between detection performance and computational cost. At the same time, the proposed method possesses a certain degree of generalizability, maintaining good performance across different radar parameters. The results of simulation experiments confirm the aforementioned advantages.

Direction of arrival (DOA) can be estimated through sparse recovery (SR) methods based on the sparsity of signals. However, conventional SR-DOA methods, such as the alternating direction method of multipliers (ADMM), encounter issues such as difficulty in parameter setting and insufficient estimation accuracy. ADMM-Net is a neural network that combines ADMM with deep unfolding (DU). In this letter, we propose a method for estimating DOA using ADMM-Net. The simulation results demonstrate that the proposed method achieves a better balance between DOA estimation performance and real-time constraints.

Rotating synthetic aperture radar (RoSAR) employs rotating antennas to provide a 360° panoramic image of the surroundings from a stationary platform. A multichannel in azimuth RoSAR antenna has been used; the system transmits chirp signals with a low pulse repetition frequency (PRF), and all channels simultaneously receive the echo, resulting in an image sequence with the same frame rate as the video. In the main lobe of the azimuth beam, the recorded echo of the single channel is ambiguous, which makes steering vector errors inevitable in real systems and causes both nonadaptive and adaptive beamforming to perform noticeably worse or even fail. In this letter, we propose a Doppler ambiguity suppression algorithm with the doubly constrained robust Capon beamformer (DCRCB) for multichannel RoSAR that overcomes a number of nonideal factors such as amplitude–phase errors and array element displacement errors between channels. In order to obtain a high-resolution scene image, RoSAR imaging procedures are performed. To clearly demonstrate the proposed approach, the experimental results using simulated data are shown.

One concern in Pulse-Doppler (PD) processing for airborne radar is range ambiguity resolving. This paper presents a method for collaborative range-ambiguity resolving among multi-radar using a single pulse repetition frequency (Mono-PRF). The approach eliminates the need for designing multiple PRF sets to resolve range ambiguity. Instead, it acquires the target’s ambiguous range in each radar coordinate system and extends the range based on the maximum ambiguity number. Subsequently, collaborative localization equations are applied to co-locate the same target from multi-radar, resulting in a set of range-angle curves in the reference radar’s coordinate system. The intersection of these curves provides a collection of range points, among which the true target range lies. Finally, a chi-square test based on Mahalanobis distance is used to select the true target point. Other radars utilize collaborative localization equations to resolve range ambiguity. Simulation results validate the effectiveness of this approach.

Measuring target angles is a crucial issue in airborne radar detection. Traditional dynamic programming track-before-detect (DP-TBD) often operates in low signal-to-noise ratio (SNR), posing challenges to angle measurement and localization. This letter presents an angle estimation technique for TBD processing. It interprets target angles across multiple frames using initial frame angle (IF-AG) and inter-frame angular rate of change (IF-ARC). Compensating for the latter and performing noncoherent accumulation to improve SNR before estimating the IF-AG enhance multiframe target angle estimation precision. The method initially formulates the cost function for multiframe angle measurement based on the raw data extracted from the multiframe array by DP-TBD. It subsequently estimates and compensates for IF-ARC, related to velocity, followed by IF-AG estimation, providing target angles for all frames concurrently. Simulation results validate the effectiveness of this method.

The angular systematic error of multichannel airspace rotating radars can be precisely compensated, while the angular random error must be improved by increasing the signal-to-clutter-plus-noise ratio (SCNR). However, the echoes of the fast airspace rotating radars are subject to robust clutter effects, Doppler scattering and target energy scattering, which reduce the output SCNR. To solve this problem, an advanced ISTAP-based multichannel radar direction-of-arrival (DOA) estimation method is proposed. Through echo modeling and analysis of airborne multichannel radar with airspace rotation, a perturbation matrix is introduced to accurately characterize the rotation effect to improve clutter suppression. Experimental results show that the proposed method can compensate the unfavorable effects caused by fast airspace rotation, improve the SCNR, and maintain the DOA estimation accuracy.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) has the capability to generate unambiguous 2-D images in the forward-looking direction. In contrast to traditional SAR systems, phase error compensation for FLMC-SAR imaging encounters the following two challenges. On the one hand, the array errors caused by the 3-D attitude angles introduce time-varying phase errors, adding complexity to the compensation. On the other hand, the spatial-domain resources used to resolve Doppler ambiguity affect time-domain SAR imaging, resulting in space-time coupling. In this article, a pulse-by-pulse Doppler ambiguity resolving algorithm for FLMC-SAR imaging based on fast factorized back-projection (FFBP) is proposed to achieve the phase errors compensation. In the proposed method, the processing of imaging and resolving Doppler ambiguity over the full synthetic aperture is decomposed into several pulse-by-pulse processing. Within this pulse-by-pulse processing framework, we modified the BP integral function after incorporating motion compensation (MOCO) and array error compensation, ultimately achieving unambiguous FLMC-SAR imaging results through the processing of “space-time combination.” In addition, the FFBP framework is utilized to accelerate the proposed FLMC-SAR imaging and Doppler ambiguity resolving. Extensive simulations and real-data experiments confirm that the proposed algorithm is capable of addressing time-varying phase errors and achieving unambiguous FLMC-SAR imaging results.

Multichannel forward-looking synthetic aperture radar (FLSAR) imaging presents challenges due to spatiotemporal coupling, especially in wide-beam scenarios. This article introduces a new polar format algorithm (PFA) called the forward-looking polar format algorithm (FL-PFA) for wide-beam FLSAR imaging. The proposed algorithm utilizes a time-space–time hybrid scheme to address the spatiotemporal coupling, matching the spatiotemporal structure of FLSAR echoes. Furthermore, the scheme combines spatial-variant motion compensation (MOCO) and polar format imaging, resulting in an efficient processing flow. For spatial-variant MOCO, this article introduces an adaptive subaperture topography- and aperture-dependent (ASATA) algorithm, offering the advantage of balancing compensation accuracy and efficiency using adaptive optimal subapertures. Subsequently, a novel PFA is proposed to obtain a focused FLSAR image. In the PFA, the azimuth angle wavenumber considers both the spatial-variant quadratic phase and residual motion errors, aligning with the spatial-variant Doppler modulation rate characteristic of FLSAR. By resampling the azimuth angle wavenumber, a well-focused FLSAR image on the polar coordinate grid and more accurate MOCO can be achieved simultaneously. Finally, extensive experiments using simulated and actual synthetic aperture radar (SAR) data demonstrate the superiority of FL-PFA.

Aircraft detection is a valuable but challenging task in synthetic aperture radar (SAR) automatic target recognition (ATR). Because of the complicated electromagnetic imaging mechanism of SAR, the SAR image of aircraft appears as a distributed collection of discrete scattering points that varies significantly with imaging conditions, like different incident angles, bringing great challenges to existing CNN-based detection methods for accurate aircraft detection. To address these challenges, we analyze and leverage the scattering characteristics of multi-scale SAR aircraft to propose a novel SAR aircraft detector named deformable scattering feature correlation network (DSFCN). First, to deal with the discreteness of SAR aircraft, we propose a new Transformer-based backbone named scalable Swin Transformer backbone (SSTB) to replace a conventional CNN-based one, to effectively extract hierarchical scattering features from multi-scale aircraft. Second, to cope with the varying image appearance of SAR aircraft, we design a deformable region correlation module (DRCM) to flexibly correlate strong scattering regions that carry aircraft salient features. Various interpretable experiments conducted on a real-measured Gaofen-3 SAR aircraft dataset demonstrate the superiority and reliability of our DSFCN over other representative CNN-based methods.

The attributed scattering center (ASC) can provide a concise physical description of radar targets, which has broad applications for physical-induced radar recognition. However, the ASC extraction is of great difficulty due to its high-dimensional parametric modeling. In this letter, a modified Alternative Direction Method of Multipliers (ADMM) unfolded-net is proposed for ASC decomposition of synthetic aperture radar (SAR) targets. By transforming the ASC decomposition challenge into a sparse reconstruction problem, the modified ADMM-net can fast attain ASC parameters as well as reconstruct a complete signal from sparse observations. Instead of tremendous iterations with fixed hyper-parameters as traditional methods, the modified ADMM-net unrolls several iterations into a few layers with learned hyper-parameters. Therefore, the proposed algorithm can realize fast and precise ASC decomposition. Experimental results confirm its effectiveness and superiority.

In order to enhance radar target recognition performance of high resolution range profile (HRRP) under low signal-to-noise ratio (SNR), a novel HRRP sequential recognition method utilizing the correlation among scattering centers is proposed in this letter. In this method, the correlative information among the prominent scattering centers is considered in covariance matrix and the sequential recognition is carried out through the long short-term memory (LSTM) network to fully excavate the information of HRRPs. Moreover, we introduce a noise-robust recognition algorithm to renew the Gaussian trained model by the estimated variance of the noise. Experimental results indicate that the proposed method can acquire higher recognition rates and better robustness by introducing correlative information and sequential processing.

Detection of highly maneuvering targets often suffers from the problem of range migration (RM) and Doppler frequency migration (DFM) within coherent processing interval (CPI), which results in performance degradation in coherent integration. In this letter, we propose a new coherent integration method which is based on neural networks and generalized Radon-Fourier transform (GRFT). Specifically, this method develops a neural network that can infer the target trajectory from the radar echo, and the trajectory reduces the search ranges of the GRFT method’s parameters according to some judgment criteria. After that, the RM and DFM are compensated via GRFT, and thereby it improves the performance of coherent integration. Besides, we introduce a customized regularization loss into the development of the neural network, which improves the detection performance. The superiority of the proposed method over GRFT is that it reduces the computational cost significantly with similar detection performance, which is confirmed by the results of experiments.

Radio frequency interference (RFI) has become a serious problem for synthetic aperture radar (SAR) systems. In recent years, numerous methods have emerged for the suppression of radio frequency interference (RFI). These methods often rely on assumptions regarding the narrowband characteristics and low-rank nature of radar echoes or interference signals. However, these assumptions may not hold true when dealing with complex RFI. To mitigate complex interference in dual-polarization (dual-pol) synthetic aperture radar (SAR) systems, a proposed method is the utilization of a filtering algorithm based on sidelobe cancellation (SLC) technology. This method leverages the discrepancies in gain and coherence between observed targets and RFI signals in the co-polarization (co-pol) and cross-polarization (cross-pol) channels. It suppresses interference through SLC filtering on a range line by range line basis in the focused image domain, serving as a post-processing step. The method is capable of tackling multiple complex interferences optimally under the least mean square criterion, such as narrowband, wideband, and noise-modulated pulsed interferences. Both simulation and real data experiments show that the method can remove RFI artifacts and effectively recover the desired SAR images.

The Video synthetic aperture radar (VideoSAR) technique is capable of providing high frame rate imaging. Through tracking the target shadow in a high resolution VideoSAR image sequence, moving target location and recognition would be achieved potentially. However, surrounded by complex clutter, moving targets’ shadows usually involve blurring, making shadow tracking difficult in real VideoSAR applications. To address clutter interference, this letter develops a shadow tracking framework based on the discriminate correlation filter (DCF) and the interacting multiple model (IMM) filter. Within this framework, the integration of DCF with the two-parameter constant false alarm rate (2P-CFAR) detector enables the assessment of the target’s appearance state for appropriate updating, mitigating the degradation of appearance templates. Moreover, a dynamic threshold is incorporated into 2P-CFAR, leveraging the historical information of the IMM motion model to establish a prior probability threshold, thereby reducing false alarms. Lastly, the integration of prior road information with the IMM model further enhances the reliability of the IMM prior motion model and indirectly adapts the threshold, effectively mitigating the impact of roadside clutters. Comparative experiments confirm that the proposal outperforms other rivals in tracking performance.

With the development of space resources, high-precision positioning is a significant way to obtain the on-orbit status information of space targets. Ground radar interferometric is an active space target positioning method, which can provide positioning information in all-weather and all-time. However, the directly measured interferometric phase will be ambiguous when the range difference between space targets and radar sites exceeds a certain boundary. In this letter, phase ambiguity resolution (AR) is established as a hidden Markov model (HMM) dynamic optimization problem in combination with the space target's movement process at different pulse times, in which no auxiliary baseline is required compared with traditional methods. And the Viterbi algorithm is used to solve the optimal ambiguous number (AN) sequence. Since the sequence decoding is mainly based on solving the optimal AN path that to explain the target position at different times best, a transmission technique of staggered carrier frequency signals is adopted, which significantly increases the robustness of phase AR. Extensive simulation experiments show that the proposal performs effectively and accurately for high-precision space target positioning.

Abnormal dynamics awareness of space targets is critical for space surveillance. With the intrinsic range-Doppler projection mechanism, an inverse synthetic aperture radar (ISAR) image sequence naturally has crucial potential for abnormal dynamics interpretation on uncooperative targets. In this paper, we develop an automated deep neural network architecture for abnormal dynamic recognition of space targets from ISAR image sequences. With the accommodation of the stacked sparse autoencoder (SSAE) network joint sparsity constraint, the geometrical feature flow of an ISAR image sequence is learned to represent the target dynamics concisely. Then, the abnormal dynamic recognition is turned into a sequential classification task by exploiting the encoded feature flow through the long-short time memory (LSTM) network. Extensive experiment results confirm the superiority of the proposal in both precision and efficiency aspects.

Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is of the capability to achieve unambiguous 2-D images in the forward-looking slight direction. FLMC-SAR imagery usually suffers from relatively low spatial resolution as only limited Doppler diversity can be generated from the synthetic aperture. In this article, a sparsity-driven resolution enhancement algorithm is proposed to improve the resolution FLMC-SAR image of the forward-looking area. Different from conventional beamforming processing to resolve the FLMC-SAR left–right ambiguity, a Bayesian sparsity reconstruction optimization is developed for jointly ambiguity resolving and resolution enhancement in the azimuth angle image domain. The spatial structure of the target in the preliminary image domain is used as the signal sparsity with prior information to solve the constrained optimization problem for FLMC-SAR image resolution enhancement. A local least square estimator of the prior noise and signal statistics in the FLMC-SAR nonisotropic image is established in terms of determining the sparsity weight parameter. Extensive simulation and real FLMC-SAR data experiments confirm that the proposed algorithm is capable of achieving the unambiguous and resolution-enhanced FLMC-SAR image.

High-squint and wide-beam Synthetic Aperture Radar (SAR) imaging is challenging for current popular SAR imaging algorithms because the severe spatial-variant motion error precludes the precise Motion Compensation (MOCO). Especially the azimuth-variant motion error (AVME) would bring not only the azimuth-variant phase error but the non-negligible Nonsystemic Range Cell Migration (NsRCM). This paper proposes a novel SAR imaging algorithm to deal with the NsRCM and the azimuth-variant phase error in high-squint and wide-beam SAR imaging. For Range Cell Migration Correction (RCMC), a Parametric Keystone Transform Algorithm (PKTA) introducing the three-axis trajectory deviations as parameters is developed. It can correct the nominal RCM and the NsRCM through a time-variant scaling transform along slow time. The robust RCMC paves the way to precisely compensate for the azimuth-variant phase error. Following, a fast and precise subaperture MOCO algorithm, which applies the Recursive Discrete Fourier Transform (RDFT), is embedded in the azimuth focus procedure to adjust the azimuth-variant phase error. The proposed algorithm can handle the high-squint and wide-beam SAR data with severe motion errors based on these improvements. Finally, extensive experiments with simulated and real-measured SAR data demonstrate the proposal’s superiority and robustness.

Due to the flexible configuration and maneuvering platform, bistatic synthetic aperture radar (SAR) plays an important role in modern remote sensing applications, but the non-ideal track simultaneously introduces model mismatch and spatial-variant phase problems. This paper proposes a sub-aperture parametric polar format algorithm (PFA) for high-resolution bistatic spotlight SAR imaging. First, a bistatic parametric polar format algorithm is proposed to focus on the generally configured bistatic SAR data. A high-precision range model in the bistatic range and ellipsoid parameter angle coordinate space is established to modify the PFA interpolation kernels. To further enhance the azimuth resolution, the PFA sub-images are fused in the image domain based on the coordinate transformation between the unified Cartesian coordinates and local imaging polar coordinates. During the sub-image fusion, in order to ensure the accurate projection and non-aliasing spectrum, we also consider the geometric deformation of the coarse PFA image and analyze the wavenumber support region along with the Nyquist sampling requirement. This novel sub-aperture method, which is theoretically more efficient than the fast back-projection algorithm, alleviates the limitation of full aperture resolution on PFA’s depth of focus. Finally, our method is applied to the general bistatic spotlight SAR data, involving the level-flight airborne transmitter and the dive hypersonic vehicle-borne receiver, and the results of both points and distributed targets demonstrate its effectiveness and efficiency.

Millimeter-wave (MMW) imaging techniques have been widely used in the public security industries for their under-controlled privacy concerns and no health hazards. However, since MMW images are low resolution and most objects are small, reflection-weak, diverse, suspicious object detection in the MMW images is a very challenging task. This paper develops a robust suspicious object detector for the MMW images based on the Siamese network integrated with the pose estimation and image segmentation, which estimates the coordinates of human joints and segments the complete human images into symmetrical body part images. Unlike most existing detectors, which detect and recognize suspicious objects in MMW images and require a complete training set with correct annotations, our proposed model aims to learn the similarity between two symmetrical human body part images segmented from the complete MMW images. Furthermore, to decrease the misdetection caused by the restricted field of view, we further fuse the multi-view MMW images observed from the same person by designing a decision-level fusion strategy and feature-level fusion strategy based on the attention mechanism. Experimental results on the measured MMW images show that our proposed models have favorable detection accuracy and speed in practical application and thus prove their effectiveness.

Random stepped-frequency chirp waveform (RSFCW) is capable of achieving high-resolution synthetic aperture radar (SAR) imagery as well as certain electronic countermeasure capabilities. The Doppler-dependent phase incoherence due to the platform motion leads to degradation of the frequency synthesis of RSFCW. In this letter, a coherence processing algorithm for RSFCWSAR is developed with full cell Doppler correction (FCDC). The waveform structure of RSFCW is exploited to decompose the signal into different Doppler cells, which paves a way to the FCDC in phase alignment for frequency synthesis. Extensive simulation has confirmed the superiority of our method’s performance in comparison to those of traditional techniques.

In recent years, deep convolutional neural networks (CNNs) pose superior synthetic aperture radar target recognition (SAR-TR) performance. However, CNN-based SAR classifiers would be vulnerable to adversarial attack (AA) when strong nonlinearity of CNN is contrapuntally utilized by AA. The AA can cause a CNN classifier to produce erroneous predictions with extremely high confidence by injecting a tiny adversarial perturbation to the input SAR images. In this letter, an accelerated SAR-TR AA algorithm is proposed named Fast C&W. We introduce a well-trained deep encoder network to replace the process of searching for the optimal perturbation of the input SAR image iteratively in the vanilla C&W algorithm. In this way, an adversarial perturbation can be generated much faster through the rapid forward mapping during an attack. Meanwhile, as a feature extraction network, the encoder network can learn the separable data region by optimizing the attack loss function. Through the encoder network, the added perturbation energy can be mainly concentrated on a region of target instead of background clutter area. This property would be of advantages in the perturbation location control in an SAR image. In the experiments, we use the proposed AA algorithm to interfere with the deep CNN-based high-accuracy SAR-TR model trained on the moving and stationary target acquisition and recognition (MSTAR) data set, which demonstrates its excellent effectiveness and thousands of times of efficiency improvement.

The higher bandwidth inverse synthetic aperture radar (ISAR) can obtain the higher resolution radar images, which can provide more target information and help improve radar target detection and recognition. It is essential to study how to achieve a precise high-resolution (HR) ISAR image utilizing limited measurement echoes. The existing neural-network-based ISAR imaging methods extract features only from limited measurement echoes, and the common features in HR ISAR images are not utilized sufficiently, which limits the imaging performance improvement. Moreover, in their loss functions, there are no explicit constraints on the correct recovery of strong scattering points, which are important in reflecting the target characteristics. In this letter, we propose a conditional probabilistic generative model to achieve the HR ISAR imaging. By optimizing the well-designed Kullback–Leibler (KL) divergence between conditional prior and approximate posterior probability distribution in the loss function, the common features contained in training HR radar images can be learned, and a suitable prior probability distribution for the latent variable can be obtained. To accurately recover the positions and relative amplitudes of strong scattering points, we blend a similarity measurement that is sensitive to the large values’ locations in a vector with the adversarial loss. Both visual and numerical results of extensive experiments prove that the proposed model can obtain enhanced effectiveness and efficiency compared with some counterparts.

The main difficulty of synthetic aperture radar (SAR)-optical image matching or registration lies in the significant heterogeneous characteristics introduced by the different imaging mechanisms between SAR and optical images. Instead of directly using the raw image pair, transforming the pair into a feature domain, where they have homogeneous feature representation, is believed more effective. Inspired by image segmentation, we develop an end-to-end deep learning model for the SAR-optical matching, based on a siamese U-net with a fast Fourier transform (FFT) correlation layer. First, the siamese U-net with sharing weights extracts the feature maps of the SAR and optical images and projects the heterogeneous images into a homogeneous space. Then, the two feature maps are cross-correlated or normalized cross-correlated by the FFT layer and a similarity heatmap is obtained. Finally, the heatmap is send into a softmax2d classifier to determine the best matching, and thus matching is converted into classification. The nonlinear mapping capability of deep learning can well tackle the intensity variation across the different imaging modals; the encoder–decoder architecture with skip connections in the U-net can take full advantage of the global information and simultaneously preserve the local resolution and position information and thus guarantees high accuracy and robustness; besides, the FFT correlation is helpful for the efficiency improvement and training with large image pairs. Experiments show that the proposed method can achieve a pixel-level matching error.

Video synthetic aperture radar (VideoSAR) provides the potential of high-resolution target images with also high frame rate. Different from the target signal in VideoSAR, target shadow straightforwardly indicates its location and track without suffering from the Doppler shift and smearing induced by the motion modulation. The target shadow can be exploited to realize the real-time tracking of the moving target with VideoSAR. In this letter, we propose a robust single target shadow tracker, which integrates the discriminative correlation filter(DCF) and interacting multiple model probabilistic data association filter(IMM-PDAF) to simultaneously overcome the interference of around clutter and target maneuvers. Considering the dependence between target shadow appearance and its dynamic motion, a filtering template updating strategy based on the estimated mode probability was proposed. VideoSAR tracking experiment shows that the proposed algorithm outperforms conventional visual tracking techniques in the robustness aspects.

Under the special geometric model of bistatic forward-looking synthetic aperture radar (SAR), the 2-D spatial variation problem makes it difficult for traditional algorithms to achieve well-focused imaging, which limits the imaging area. In this letter, from the perspective of range-azimuth decoupling of signal, dechirping processing and keystone transform are used to realize range-variant phase compensation and range cell migrations (RCMs) correction. Based on the range-azimuth decoupled signal, the overlapped subaperture processing can eliminate the azimuth-variant quadratic chirp rate phase in each range cell. Experimental results demonstrate that the proposed method is suitable for the bistatic forward-looking SAR (BFSAR) system to solve the 2-D spatial variation problem and achieve well-focused imaging.

Current subaperture-based azimuth-variant motion compensation algorithms for synthetic aperture radar (SAR) imagery usually suffer from the challenge of keeping high precision and efficiency simultaneously. In this letter, a novel motion compensation approach is developed to precisely correct the azimuth-variant motion errors. The proposed algorithm applies a time-domain filter to implement the precise Subaperture-to-Pulse correction, providing a promising azimuth-variant phase correction. Extensive experiments demonstrate the superiorities of the proposal with real-measured high-squint SAR data.

Minimum entropy autofocus (MEA) has been applied in unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imagery for its robustness in different circumstances. However, large amount of range cell samples to calculate the gradient for the minimum entropy optimization keeps its optimal convergence, which usually degrades the efficiency in real UAV SAR applications. In this letter, accelerated minimum entropy autofocus is proposed, which leverages both high computational efficiency and phase error estimation precision simultaneously. A strategy of stochastic gradient (SG) calculation is introduced in the MEA optimization with randomly selecting samples in each iteration through a probability distribution function (PDF). Experimental results with real UAV SAR data have validated the superior performance of the proposed SG-MEA algorithm.

The$L$-type double baseline configuration (three antennas) radar system is commonly adopted to obtain 3-D images in the traditional interferometric inverse synthetic aperture radar (InISAR) imaging, making high demands on the complexity of hardware design and signal processing. In this letter, a novel InISAR imaging framework based on single baseline configuration (SBC) (two antennas) for maneuvering targets is proposed, which can acquire the range and azimuth coordinates of the targets through transmitting wideband signals and azimuth scaling. To address the problems of error transmission and insufficient robustness in the traditional cascaded motion compensation method, a joint motion compensation and azimuth scaling (JMCAS) algorithm is developed. By maximizing the image contrast (IC), this method can perform the optimal parameter estimation of translational and rotational motion of maneuvering targets, so as to simultaneously achieve the fine motion compensation and azimuth scaling. In addition, a non-coherent fusion image registration (NCFIR) algorithm is presented to achieve the image registration between the two antennas in a vertical direction. On this basis, the height coordinates of the targets can be obtained by means of interferometric processing. Extensive experimental results from both simulated and real data corroborate that the proposed algorithm can achieve high-precision 3-D imaging of maneuvering targets with low hardware complexity at a low cost.

Formation targets have the characteristics of close distance, RCS difference and strong motion correlation. Radar should have enough resolution and anti-jamming ability to detect formation targets in the complex electromagnetic environment. The stepped frequency signal can be synthesized into a large bandwidth signal, so as to improve the range resolution and enhance the recognition performance of formation targets. In order to detect small targets next to large targets in formation targets, the signal needs to have low sidelobe characteristics. In this letter, we propose an optimized stepped frequency signal that can be synthesized into a large bandwidth with low autocorrelation sidelobes and a stopband to against narrowband interference. The proposed method can well detect small targets next to large target in formation, and has a certain narrowband anti-jamming ability. The simulation experiment results verify the advantages of the proposed waveform.

This letter proposes a modified polar format algorithm (PFA) applied to the highly squinted synthetic aperture radar (SAR) onboard an accelerating hypersonic platform. In conventional PFA, the range wavenumber is orthogonally decomposed to obtain the 2-D wavenumbers corresponding to the imaging Cartesian coordinates. This operation, however, is inadequate when facing with more complex motion trajectory. In this letter, the essence of the proposed method is to homogenize the spatially nonuniformly sampled echoes by combining interpolation with the generalized wavenumber definition. The imaging polar coordinate system in the slant plane provides new space-wavenumber Fourier transform pair, and the 1-D interpolation compensates for the range-variant phase errors. The proposed algorithm, thus, enables fast and effective imaging of hypersonic SAR in large squint angles and high maneuverability dive mode. Its performance is discussed and analyzed in this letter, including the resolution and the size of the imaging scene. The effectiveness, superiority, and application value of the proposed algorithm are verified by simulation.

Retrieval of attitude and geometry of satellite targets from inverse synthetic aperture radar (ISAR) images is an important but difficult task, because of the complex scattering phenomenon and motion-dependent projection mechanism. In this letter, we construct a novel component extraction network (CEN)-based oriented object detection to obtain the projection parameters of the target components from ISAR images, then combine this CEN with particle swarm optimization (PSO) algorithm to retrieve the 3-D attitude and geometry of the target components. This proposed method can be used to accurately estimate the attitude and geometry of the target. The simulation experiments show the effectiveness and superiority of this method.

Real-time attitude estimation of on-orbit spacecraft is a core task in various space applications. Most of the existing methods are based on long-term observation by high-resolution sensors, such as space-borne cameras and ground-based radars. However, when the observation period is limited, it is difficult to obtain target instantaneous attitude information by these methods. To achieve instantaneous attitude estimation from a single camera image, a U-Linked network (ULNet) is proposed in this work. The prior structural constraints of key points are used to reflect the relationship between three-dimensional (3D) target attitude parameters and two-dimensional (2D) images. In this way, target attitude estimation can be solved through the feature point regression when the large-perspective image dataset can be built. The simulation results confirm the feasibility of the proposed method. Besides, the estimation performance of the proposed method also is investigated under different imaging observation conditions.

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, “GKT-JSVAAS-GKT. ” This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

Under high sea state, ship targets make complex maneuvering motions due to strong disturbances such as sea waves and sea winds. Selecting the optimal imaging time interval to shorten the coherent processing interval (CPI) can reduce the complexity of motion errors, but the imaging resolution is affected as well, i.e., the motion error complexity and imaging resolution are constrained by each other. To address this problem, this article proposes a novel inverse synthetic aperture radar (ISAR) imaging framework for complex maneuvering ship targets to achieve the integration of super-resolution (SR) ISAR imaging and fine motion compensation (ISRFMC) under high sea state. With regard to the complex maneuvering motion of ship targets, we analyze the motion errors caused by the time-variant rotational velocity and imaging projection plane (IPP) on the echo signals, respectively, and a fine phase error model is established to uniformly represent the dual time-variant characteristic (DTVC) of complex maneuvering ship targets. Moreover, a deformed Akaike information criterion (DAIC) is developed to realize the adaptive selection of the phase error model with the image sharpness as the objective function. Underpinned by the Bayesian compressive sensing (BCS) theory, the SR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Particularly, the fine phase errors are constructed as the model errors of image reconstruction, and the particle swarm optimization algorithm (PSO) is utilized to solve the maximum image sharpness optimization problem in order to perform the joint fine motion compensation and azimuth scaling (JFMCAS). ISRFMC or the integration of SR ISAR imaging and fine motion compensation can be achieved through alternate iteration, so as to obtain well-focused and scaled high-resolution ISAR images of complex maneuvering ship targets under high sea state. Extensive experiments based on both simulated and real data verify that the proposed algorithm is capable of addressing the conflict between imaging resolution and motion error complexity under high sea state.

The high orbit height and long coherent processing interval (CPI) of geosynchronous (GEO) targets lead to the problems of ultralow signal-to-noise ratio (ULSNR) and complex signal modulation, posing great challenges to the traditional radar target detection and imaging algorithms. To address the problems, this article proposes a novel model-data codriven integration algorithm of detection and imaging for GEO targets with wideband radar. In this technique, underpinned by the transformation relationships between multiple spatial coordinate systems and the orbit prior information of GEO targets, we deduce the analytical expressions of the effective rotational vector of GEO targets so as to accomplish the model-driven optimal subaperture selection for integration of detection and imaging (OSASIDI). This considerably improves the processing performance and algorithm efficiency compared with traditional data-driven methods at ULSNR. In addition, we derive the radar equation of GEO targets for integration of detection and imaging in detail, which guides OSASIDI by analyzing the impacts of different parameters on detection and imaging performance. Aiming at the complex signal modulation problem caused by ultralong CPI (ULCPI) during the optimal subaperture (OSA) at ULSNR, we innovatively propose a model-data codriven integration of detection and imaging algorithm (MDCDIDI), which can eliminate the complex spatial-time-variant motion errors caused by the dual time-variant characteristic (DTVC) of effective rotational vector, so as to realize the focus-before-detection and obtain the well-focused inverse synthetic aperture radar (ISAR) images. Extensive experimental results from simulated data, which are generated from actual GEO parameters and the computer-aided-design (CAD) model of the Tiangong-I (TG-I) satellite, corroborate the effectiveness of the proposed algorithm.

Traditional radar waveforms are easily intercepted and interfered with by enemy’s reconnaissance system with the time–frequency periodic pattern recognition. Frequency and pulse repetition frequency (PRF) agility is an effective approach to decrease interception probability and increase anti-jamming capabilities. On the other hand, the agility brings about high sidelobes and the difficulty of parameter estimation using the range-Doppler signal processing. In this article, an optimization and high-resolution imaging algorithm for sparse stepped linear frequency modulation waveform (SSLFMW) with frequency and PRF agility is developed. The range and Doppler 2-D autocorrelation function of the agile waveform is investigated to pave a way to find an optimization strategy for frequency and PRF to suppress range and Doppler sidelobes. Relied on the pulse trains of low Doppler sidelobes, we propose a method of cognitive transmitting and motion retrieval based on the maximum likelihood principle to eliminate the frequency and range coupling in velocity estimation. The 2-D sparse reconstruction with conjugate gradient solver is proposed to efficiently reconstruct the high-resolution range-Doppler image with the frequency and PRF agility waveform. Both simulated and real-measured data sets are used to verify the improved performance of the proposal.

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

In recent years, deep neural networks are increasingly popular in the field of radar high-resolution range profiles (HRRPs) target recognition. Unfortunately, recent researches have revealed that a deep-learning classifier can be easily fooled by adding small perturbations to the input, named adversarial attack. This provides us an inspiration for radar deceptive jamming signal generation in electronic countermeasures (ECMs). However, the perturbations generated by these adversarial attacks are usually of complex envelopes and quite low power, making it challenging for jammers to generate such actual jamming signals. To solve that issue, we propose a practical deceptive jamming generation method that learns the vulnerable range cells in an HRRP sample and injects several jamming pulses with specific amplitudes into these range cells. Such jamming signals are easy to generate and can deceive the radar automatic target recognition (RATR) model to output the wrong target category prediction with high confidence. To avoid the requirement of the recognition network structure information, we leverage the differential evolution optimization algorithm (non-gradientbased). Further, to provide the potential of real-time jamming signal generation during the test, an encoder is constructed not only to learn the separable features but also to find the vulnerable range cells and the specific amplitudes of the jamming pulses. In the experiments, we apply the proposed attack algorithms to fool the one-dimensional convolutional neural network-based HRRPRATR models. The extensive experimental results on measured aircraft HRRP dataset prove that the proposed algorithms achieve a promising attack performance and serve as a practical and fast deceptive jamming generation method.

The quality of inverse synthetic aperture radar (ISAR) images suffers seriously from the two-dimension (2-D) resolution and noise. The motion errors arising from translational and rotational motion further aggravate the image defocusing. For the limited bandwidth and short aperture (LB-SA) signal, this letter proposes a novel 2-D joint super-resolution (2D-JSR) ISAR imaging with joint motion compensation and azimuth scaling (JMCAS) algorithm. In this technique, a 2D-JSR signal model is established, enabling the 2-D high-resolution ISAR image to be generated by solving a sparsity-driven optimization problem with a modified quasi-Newton solver. In addition, a new JMCAS algorithm is developed to enhance the focusing performance of image. Not only can this algorithm jointly correct the range shift and phase error caused by translational motion, it can also complete the azimuth scaling and range spatial-variant phase error (RSVPE) compensation simultaneously. Through the iterative processing of 2D-JSR reconstruction and JMCAS, the well-focused and scaled high-resolution ISAR image can be obtained. Both simulated and real data experiments are provided to verify the effectiveness of the proposed algorithm.

Attitude determination of space target from an inverse synthetic aperture radar (ISAR) image sequence is an important but difficult task, because of the complex electromagnetic scattering characteristics of the target. To autonomously estimate the attitude of the space target from the ISAR image sequence, this letter introduces the key point feature extraction network (KPEN), and proposes a novel method for attitude determination via the key point feature extraction. The geometric relationship between the target attitude parameters and the key point feature is established through range-Doppler (RD) projection matrix. With the help of the key point extraction network, key point feature is extracted from ISAR image. Then the target attitude and its component size are autonomously recovered through key point feature by solving a nonlinear optimization. Extensive experiments show the effectiveness and superiority of the proposal.

Synthetic aperture radar (SAR) as a wideband radar system is subject to complicated interferences, such as radio frequency interference or other narrowband interferences (NBIs). In order to suppress the NBI, voluminous literature focused on its signal models and characteristics, such as the sinusoidal model and relatively constant frequencies. However, in practice, the interference environment is commonly complicated. It is hard to model the interferences accurately and mitigate them clearly in an easy way, especially for the time-varying interferences. In this article, a novel graph-based algorithm is proposed to mitigate the time-varying NBIs by using graph theory, which constructs the connections between different azimuth samples of NBIs. As a result, the locally time-varying interferences can be clustered in a nonlinear low-dimensional manifold and effectively removed by the proposed algorithm. In addition, the case of the globally time-varying interference is also analyzed in detail with strict derivations to demonstrate its low-rank property. Furthermore, the matrix factorization scheme is introduced to improve the efficiency of the proposed algorithm, and the closed-form solutions are derived for each iteration. The real SAR data with measured NBIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system.

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.

For the interference suppression problem on synthetic aperture radar (SAR) systems, traditional methods have focused on how to remove one kind of interference through nonparametric methods and parametric methods. However, complicated interferences, including both narrowband interferences (NBIs) and wideband interferences (WBIs), severely affect SAR imaging in practical scenarios. Also, the spectra of the complicated interferences can be continuously distributed, which are even harder to mitigate from the received signal. Hence, in this article, we propose a smoothing multiview (SMV) tensor model in range-azimuth-space domain to represent the intrinsically unified characteristics of the NBIs and the WBIs for SAR systems, reserving more azimuth degrees-of-freedom (DOFs) than the previous MV tensor model. The proposed SMV tensor model can enhance the potential low-rank property of the complicated interferences, even though the interferences may be continuously distributed in low-dimensional domains. Moreover, due to the larger scale of the SMV model than those of the traditional models, a complex reweighted tensor factorization (CRTF) algorithm is proposed to factorize the large-scale tensor into the product of two small-scale tensors, achieving both better computational efficiency and better low-rank approximation of complicated interferences. Finally, the measured SAR data with different kinds of simulated complicated interferences are employed to demonstrate the effectiveness and efficiency of the newly designed SMV model and the proposed method compared with the MV model and the complex tensor robust principal component analysis (CT-RPCA) method.

Traditional synthetic aperture radar (SAR) imaging is limited to achieve the high-resolution image of the side-looking areas. Nevertheless, equipped with a small size linear array across the trajectory, forward-looking multichannel SAR (FLMC-SAR) is capable of reconstructing the high-resolution image of the front area. In FLMC-SAR imaging framework, the left-right Doppler ambiguity is expected to resolve with beamforming approaches using the multichannel system diversity. However, beamforming-based Doppler ambiguity resolving is sensitive to the array deviation angle, which causes a mismatch between the azimuth angle and Doppler frequency. In this article, we propose an array deviation angle calibration and imagery algorithm for FLMC-SAR. The space-time characteristic of FLMC-SAR is explored and the range-dependent array deviation angle model is established. Following the Doppler beam sharpening imaging, strong targets are selected to derive the mismatch of the space-time characteristic. A maximum likelihood estimation of the array deviation angle is developed to modify the matching between the azimuth angle and Doppler frequency. Therefore, the left-right Doppler ambiguity can be solved correctly, yielding high-resolution FLMC-SAR imagery. Extensive simulation and real data experiments are performed to demonstrate the effectiveness of the proposed method.

With regard to the multifunction radar transmitting sparse stepped-frequency-modulation (SSFM) signal for inverse synthetic aperture radar (ISAR) imaging, the received echo signal is usually sparse in two dimensions, i.e., sparse stepped-frequency-modulation and sparse aperture waveforms (SSFM-SAWs), and there are translational and rotational motion errors between subpulses. The two problems seriously challenge the feasibility of conventional 1-D sparse reconstruction algorithms. This article proposes a novel high-resolution ISAR imaging and motion compensation with the 2-D joint sparse reconstruction (2D-JSR) algorithm. In this technique, a 2D-JSR dictionary is established according to the SSFM-SAW signal model. Based on the Bayesian compressive sensing (BCS) theory, the 2D-JSR is then transformed into solving a sparsity-driven optimization problem with l1-norm constraint. With the accommodation of a modified quasi-Newton solver, the exact recovery of SSFM-SAW can be achieved. In addition, a new algorithm, named joint translational motion compensation and range spatial-variant autofocus (JTSVA) algorithm, is also developed to realize motion parameters by a two-step estimation. Integrating with 2-D coupling information of echo signal and the efficient and robust motion compensation algorithm, the accurate motion parameters together with well-focused and scaled high-resolution ISAR images can be obtained. Extensive experiments based on both simulated and real data demonstrate that the proposed algorithm is capable of the precise reconstruction of ISAR images and the effective suppression of both motion errors and noise.

Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target's scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance.

The stepped-frequency waveform is usually used to synthesize a wideband signal in the radar imaging system. To reduce the amount of data and coherent pulse intervals (CPIs), as well as to improve antijamming abilities, the sparse stepped frequency is employed in the area of inverse synthetic aperture radar (ISAR) imaging. Nevertheless, the traditional sparse stepped linear frequency modulation waveform (SSLFMW) has a shortage of high grating lobes caused by missing frequency bands, resulting in a degradation of the ISAR imaging quality. Many methods have been proposed to reduce the effect of grating lobes by echo signal processing. However, the method of grating lobe reduction is rarely studied from the aspect of waveform optimization. In this article, a novel SSLFMW with the low grating lobes in the ISAR imaging system is proposed. By deriving the autocorrelation function (ACF), the relation between grating lobes and waveform parameters, including stepped-frequency and phase-coded elements, is established. An optimization method based on alternate iteration is designed to optimize waveform parameters and reduce grating lobes. Based on this optimized SSLFMW, we establish an ISAR imaging framework with the compressive sensing (CS) theory. Finally, the experiments are designed to show that the optimized SSLFMW has lower grating lobes. Both simulated and real measured data are used to prove that the optimized waveform has a better performance in the high-resolution range profile (HRRP) synthesis and ISAR imaging compared with the traditional SSLFMW.

Dynamic estimation of spin spacecraft is a challenge and plays a significant role in space situation awareness applications like potential space collision warning. Based on remote sensing technologies of laser and radar sensors, current methods almost adopt a match strategy to estimate the dynamic parameters of a particular target with the long-term measurement collection. These kinds of data-driven methods merely consider the inherent connection between the measured characters and target dynamic patterns, and can hardly be expanded to other spacecraft when the measurement collection is insufficient. Therefore, this article presents a novel approach to interpreting multiple-station inverse synthetic aperture radar (ISAR) images for the dynamic estimation of spin spacecraft. As a unique phenomenon of radar imaging, the imaging plane of ISAR observation not only depends on the change of the relative position between the target and radar, but also changes with the spin of the target. In order to decouple the target dynamic estimation from the determination of the imaging geometry, the angular diversity of multiple-station images is employed. The proposed algorithm deduces an explicit expression of target dynamic parameters under the imaging projection model of the multiple-station observation. By utilizing the chaotic grasshopper optimization algorithm (CGOA), it determines three crucial elements of the target spin motion with a two-step optimization, including instantaneous attitude, rotation shaft and rotation speed. Simulation experiments of a typical spin spacecraft, Tiangong-I (TG-I), illustrate the feasibility of the proposed method under different motion patterns.

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.

As more and more satellites are launched into the space, dynamic estimation of spin satellites has become a critical component of the space situation awareness application. Some explored studies using exterior measurements from different sensors such as optical device and inverse synthetic aperture radar (ISAR) to estimate dynamic parameters of spin satellites. As a single sensor normally provides two-dimensional observation, three-dimensional estimations resulting from these algorithms are strictly related to the prior knowledge of targets characteristics. As a result, it is difficult to expand these methods to other satellites. In order to support the dynamic estimation of most spin satellites, this paper presents a novel dynamic estimation approach which employs synchronized optical-and-radar images. The optical-and-radar fusion strategy has demonstrated its superiority in image analysis field, and breaks down the dynamic estimation of spin satellites into two sub-problems: target attitude estimation and spin parameters estimation. In this work, the proposed algorithm deduces two explicit expressions of target dynamic parameters under the imaging projection model of the joint optical-and-radar observation. Through the particle swarm optimization (PSO), target dynamic parameters are determined in two stages. This paper presents some experiments illustrating the feasibility of the proposed method and subsequent conclusions, which reflect advantages of the joint optical-and-radar observation mode in image interpretation.

Nowadays, in the complicated electromagnetic environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs. In this paper, we first strictly derive the low-rank property of both NBIs and WBIs and then employ the robust principal component analysis (RPCA) to simultaneously suppress them. Unlike the traditional methods, the proposed method is capable to tackle with complicated interferences, not only the isolated NBIs or WBIs. The real X-band SAR data is provided to demonstrate the effectiveness of the proposed method.

Nowadays, narrowband interferences (NBIs) severely affect the imaging quality of synthetic aperture radar (SAR) systems. Fortunately, NBIs has nearly fixed frequencies along the azimuth time and they are demonstrated to be low rank in previous studies. All the NBI suppression methods are based on one-dimensional (1-D) and two-dimensional (2-D) domains to extract NBIs from the received signal. Actually, NBIs have a special low-rank property which can be employed in three-dimensional (3-D) domain for extra spacial degrees of freedom (DOFs). Hence in this paper, we propose a reweighted tensor nuclear norm minimization (RTNNM) algorithm to efficiently and effectively mitigate NBIs via three-mode tensor structure. The proposed method employs the special low-rank property of NBIs via multiple views in range-azimuth-space domain and deals with the drawback of the tensor nuclear norm minimization algorithm. The real X-band SAR data is employed to demonstrate the effectiveness and efficiency of the proposed method.

Artificially structured metamaterials apertures antennas (MAA) enables producing the pseudorandom and spatially variant radiation fields to encode spatial information and retrieve scene images using computational imaging (CI) algorithms. Combined with synthetic aperture radar (SAR) technologies, by moving a linear shape MAA in crosswise direction, a hybrid imaging system with the combination of MAA and SAR is demonstrated in this paper. Focusing on this peculiar imaging geometry, we propose a postprocessing algorithm which combines the classic omega-k algorithm and CI algorithms to achieve fully three dimensional scene images. Compared with traditional frequency-diverse imaging reconstruction algorithms, the postprocessing is more efficient could achieve as high efficiency as SAR algorithms do. Extensive imaging simulations are conducted to illustrate the effectiveness of the proposed algorithms.

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.

Nowadays, in the electromagnetism environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs, which are widely distributed in the 1-D range frequency domain or 2-D range time-frequency domain. In this paper, we propose a complex tensor robust principal component analysis (CT-RPCA) method based on a novel 3-D range-azimuth-space tensor model to mitigate continuously distributed NBIs and WBIs simultaneously. The main contributions of this paper are summarized in three aspects. First, we strictly prove the low-rank property of the isolated NBIs and WBIs in the range-azimuth domain. Second, we use multiple views of the signal to construct a novel 3-D range-azimuth-space tensor model, where both the NBI tensor and the WBI tensor have spatial low-rank property due to the approximately stable frequency bands along the spatial dimension. Third, the CT-RPCA method is employed to efficiently suppress NBIs and WBIs simultaneously by solving the tensor RPCA problem. Finally, the real SAR data with simulated complex interferences are employed to demonstrate the effectiveness of the proposed method.

When a target is involved in maneuvering motion, the nonuniform 3-D rotation motion will cause a continuous change of image projection plane (IPP), which would induce 2-D spatial-variant phase errors. In this case, the inverse synthetic aperture (ISAR) image would be seriously blurred when using the traditional compensation methods. On the other hand, strong noise has been always challenging the conventional methods in motion parameters estimation and phase error compensation. In this paper, we propose a noise-robust compensation method to compensate the 2-D spatial-variant phase errors of the maneuvering target via using tracking information and parametric minimum entropy optimization. First, the maneuvering signal model is developed based on a 2-D spatial-variant model and a 3-D rotation motion model. Based on the developed signal model, a parametric entropy minimum optimization is established to estimate the rotation motion parameters. A gradient-based solver of this optimization is then adopted to iteratively find the global optimum. Meanwhile, in order to increase the robustness of this optimization under low SNR, an extended Kalman filter is adopted here for coarse motion estimation via using tracking information. By treating these estimated motion parameters as initial values, we can effectively prevent this optimization from trapping into a local optimum. Finally, the 2-D spatial-variant phase error can be iteratively compensated, and a well-focused ISAR image can be obtained. The proposed method has three main contributions: 1) it is applicable in the case of changing IPP; 2) it gives the exact expression of chip parameters; and 3) it can efficiently compensate the 2-D spatial-variant phase errors under low SNR. Experiments based on the simulated data and the real measured data prove the effectiveness and robustness of the proposed method.

This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden-Fletcher-Goldfarb-Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm.

For modern meter-wave radar, the performance of low-angle target altitude measurement is limited by multipath phenomenon, especially in the complex terrain environment where the multipath signal is perturbed by irregular surface. To address this problem, a practical signal model for meter-wave radar in practical terrain is first presented, where the influence of the perturbed multipath caused by irregular reflecting surface is taken into consideration. A novel compressive sensing (CS) based altitude measurement algorithm, combined with alternative optimization and dictionary updating techniques, is then proposed, in which the perturbation caused by the complex terrain can be iteratively compensated to estimate the target altitude more precisely. Numerical results based on both simulated data and real data demonstrate the effectiveness of the proposed algorithm under complex terrain environment.

This paper addresses an autofocusing technique for highly squinted airborne synthetic aperture radar (SAR) imaging. In highly squinted mode, the phase errors resulting by trajectory deviations usually exhibit both range and cross-range variance. To circumvent this problem, a 2-D space-variant phase error model is proposed. Then an autofocusing approach is presented to realize precise SAR reconstruction. This approach mainly consists of three processing steps. First, multiple local images are automatically selected based on sliding window operation in both range and cross-range dimensions. For each local image, weighted squint phase gradient autofocus kernel is applied to estimate the local phase error function. Second, the derived multiple local phase error functions are combined to resolve the 2-D space-variant phase error model using a weighted total least square method. Third, the fast factorized back-projection algorithm with pixelwise phase error correction is utilized to obtain focused image eventually. Experiments on both simulated and real-measured SAR data sets validate the focusing performance of the proposed approach.

Due to the accuracy limitation of the navigation system, deviations between the real trajectory and the measured one appear inevitably in airborne synthetic aperture radar (SAR), which degrades the image quality dramatically. To improve the focusing performance, these trajectory deviations should be well estimated and compensated. In this paper, a data-based autofocus approach is proposed to correct the residual 3-D trajectory deviations. This new approach mainly contains two processing stages. The first stage is the local phase error estimation procedure involving small images autofocusing. A gradient function considering smoothness regularization is developed to efficiently achieve the sharpness-maximizing local phase error functions. In the second stage, the local phase error functions are combined to retrieve the residual 3-D trajectory deviations by a proposed weighted total least square method. This approach has been applied on highly squinted and large-swath airborne SAR raw data, respectively. Both real data experiments generate well-focused SAR images by the estimated trajectory parameters, and thus, validate the effectiveness of the proposed autofocus approach.

Highly squinted synthetic aperture radar (SAR) data focusing is a challenging problem with difficulty to correct the severe range-azimuth coupling and motion errors. Squint minimization processing with the range-walk correction is widely adapted to simplify the decoupling processing, while it destructs the azimuth-shift invariance of conventional SAR transfer function. In this paper, a two-stage focusing algorithm (TSFA) is proposed to generate a focused imagery for the highly squinted airborne SAR. In the proposed algorithm, conventional range cell migration correction and azimuth matched filtering are performed and a fine focusing stage is established to correct the azimuth variance. In the fine focusing procedure, the coarse-focused image is divided into azimuth blocks to accommodate the correction of azimuth-variant residual range migration and phase terms. Moreover, precise motion compensation is embedded into the TSFA procedure to form an accurate airborne SAR imagery, which may be called the extended TSFA. In order to balance the processing precision and computational load, optimal selection of block size is investigated in detail. Both simulated and real measured airborne SAR data sets are used to validate the proposed approaches.

Microwave metamaterial aperture imaging radar (MMAIR) is capable of generating high resolution images without using mechanical scanning or antenna arrays. In MMAIR, metamaterial elements are specifically embeded into a parallel plate waveguide, whose resonance frequencies vary among a wide bandwidth. Different radiation fields are gained by wide-band waveforms, and the scene information are measured by measurement matrix which consists of a set of radiation modes. According to the Compressed Sensing theory, MMAIR performance is restricted by the limited frequency measurement modes. Herein we propose a rotation-synthesis approach to synthesize radiation measurement modes. By rotating the metamaterial aperture panel around the panel axis, the approach exploits the radiation field's multi-beam spatial diversity, and with the azimuth rotation, the radiation field pattern varies relative to the scene under the radiation field. As a result, significant MMAIR imaging enhancement is achieved with the crucial increase of radiation measurement modes. The simulations verify the effectiveness and image improvement of the proposed method.

The back-projection (BP) algorithm is an ideal solution for large-swath airborne SAR imaging. However, space-variant phase errors induced by trajectory deviations dramatically degrade the BP-focusing performance in a large-swath mode. In this letter, we propose an autofocus method that is compatible with the BP imagery, in which the phase-error function is constructed for individual pixels. In the new method, multiple local areas at different illumination directions within the radar beam are synthesized to trace the local phase gradient. From these phase gradient estimates, the accurate pixel-wise phase-error correction for all the contaminated pulses is achievable. This approach is capable of correcting space-variant phase errors with high precision and efficiency for large-swath SAR imaging. Experiments based on the real data that are recorded by a highly squinted SAR system validates the effectiveness of the proposed autofocus method.

Synthetic aperture radar (SAR) systems mounted on unmanned aerial vehicles (UAVs) are usually sensitive to trajectory deviations that cause serious motion error in the recorded data. In this letter, a novel range-dependent map-drift algorithm (RDMDA) is developed to accommodate the range-variant characteristics of severe motion errors. Utilizing the algorithm as a core estimate, we come up with a robust motion compensation strategy for the UAV SAR imagery. RDMDA outperforms the conventional MDA in both accuracy and robustness while it keeps similar efficiency. Real data experiment shows that the proposed approach is appropriate for precise imaging of UAV SAR systems equipped with only a low-accuracy inertial navigation system.

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Since inverse synthetic aperture radar (ISAR) imaging is a valuable technique in the identification of space satellites, it can potentially detect interesting components of space satellites in ISAR images to further conduct identification. This study proposes a novel method, defined as feature probabilistic estimation (FPE), to detect interesting components of space satellites based on ISAR image registration. In FPE, area feature registration is provoked to establish the relationship between space satellites and off‐line templates of interesting components, followed by detection accuracy based on weighted Gaussian probabilistic density function. Electromagnetic simulations with different aspects, interesting components' structures and scenery noise demonstrate the efficiency and robustness of the proposed FPE, compared with the normalised cross coefficient.

This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Interferometric synthetic aperture radar (InSAR) images are corrupted by strong noise, including interferometric phase and speckle noises. In general, the scenes in homogeneous areas are characterized by continuous-variation heights and stationary backscattered coefficients, exhibiting a locally spatial stationarity. The stationarity provides a rational of sparse representation of amplitude and interferometric phase to perform noise reduction. In this paper, we develop a novel algorithm of InSAR image formation from Bayesian perspective to perform interferometric phase noise reduction and despeckling. In the scheme, the InSAR image formation is constructed via maximum a posteriori estimation, which is formulated as a sparse regularization of amplitude and interferometric phase in the wavelet domain. Furthermore, the statistics of the wavelet-transformed image is modeled as complex Laplace distribution to enforce a sparse prior. Then, multichannel imaging is realized using a modified quasi-Newton method in a sequential and iterative manner, where both the interferometric phase and speckle noises are reduced step by step. Due to the simultaneously sparse regularized reconstruction of amplitude and interferometric phase, the performance of noise reduction can be effectively improved. Then, we extend it to joint sparse constraint on multichannel data by considering the joint statistics of multichannel data. Finally, experimental results based on simulated and measured data confirm the effectiveness of the proposed algorithm.

This letter presents the accelerated fast backprojection (AFBP) algorithm for high-resolution spotlight synthetic aperture radar (SAR) imaging. In conventional fast backprojection (FBP) algorithms, image-domain interpolation is employed in the subaperture (SA) fusion. However, in AFBP, by using a unified polar coordinate (UPC) system, the interpolation-based fusion is substituted by fusing the SA spectra in the wavenumber (WN) spectrum domain. The WN-domain SA fusion is efficiently implemented by fast Fourier transform and circular shifting. In this letter, an accurate impulse response function and the WN spectrum expression of the backprojected image in the UPC are explicitly derived, and furthermore, the implementations of AFBP are investigated in detail. Compared with conventional FBP algorithms, the AFBP can precisely focus on high-resolution SAR data with dramatically improved efficiency. Both simulation and real-measured data experiments validate the superiorities of AFBP by comparing it with the fast factorization backprojection (FFBP) algorithm.

In this letter, a novel polarimetric target decomposition (PTD) method based on the attributed scattering center (ASC) model is proposed for man-made targets in synthetic aperture radar (SAR) images. By extracting attributed parameters, polarimetric characteristics of targets can be exploited by performing PTD on the extracting parameters of ASCs instead of pixels in conventional PTD algorithms. As a result, the integrity of target components is enhanced, leading to a reliable analysis on the polarimetric scattering mechanisms of SAR targets. In the proposal, an attributed parameters extraction method based on joint exploitation of multiple polarimetric channels and a target discriminating method based on a constant-false-alarm threshold are developed to improve its robustness in strong noise scenarios. Experimental results confirm the effectiveness of the proposed algorithm.

This letter proposes a precise cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) images by estimating the effective rotation angle through coordinate locations of feature points extracted from two sequenced subaperture ISAR images. In the approach, we first extract adequate feature points and feature descriptor vectors from these two images by scale-invariant feature transform and speeded-up robust features. Then, a two-stage registering scheme is employed to match these feature points to link the two images. Consequently, the effective rotation angle is efficiently and robustly estimated by evaluating a cost function based on the coordinate locations of the matched feature points. Experiments of simulated and real signals validate this proposal.

In order to obtain high-resolution wide-swath (HRWS) images, the multichannel in azimuth synthetic aperture radar (SAR) system has been adopted to deal with the contradiction problem between high resolution and low pulse repetition frequency (PRF). In this paper, a novel channel-calibration method is proposed for the multichannel in azimuth HRWS SAR imaging system. During the channel calibration, the mismatch between the channels, which results from the gain-phase error and the range sampling time error, is first corrected by the coarse-calibration processing in the range frequency domain. Then, the along azimuth baseline measurement error is estimated. Considering the range variance in the residual phase error, the data are processed in blocks along the range time domain, and the error of every subblock data is estimated. After that, a fitting and filtering is implemented along the range to the estimated values of the phase error of all subblocks. The range-variant phase error is then compensated using their estimated values. After channel calibration, this paper also presents a new Doppler ambiguity suppression algorithm which nulls the ambiguity components in the Doppler domain. The newly proposed algorithm outperforms the post-Doppler ambiguity suppression algorithm. The airborne real measured scan synthetic aperture radar data, which are acquired by a seven-channel in azimuth SAR imaging system with the system working at X-band, are utilized to demonstrate the performance of the newly proposed channel-calibration method and the new Doppler ambiguity suppression algorithm.

Back-projection (BP) is considered as an ideal methodology for the high-resolution synthetic aperture radar (SAR) imaging. However, applying conventional autofocus techniques to BP imagery requires a special consideration and is usually difficult to implement. In this letter, we present a scheme to compatibly blending a novel multiple aperture map drift (MAMD) algorithm with fast factorized back-projection (FFBP). Through an elaborate BP coordinate, we construct the Fourier transform relationship between FFBP sub-aperture (SA) images and the corresponding range-compressed phase history data. The phase error function is achieved by the MAMD within FFBP recursions, and well-focused imagery is obtained by phase correction on the range-compressed phase history data. The proposed scheme inherits the advantages of high precision and efficiency of the FFBP, and is suitable for high-resolution spotlight SAR imaging with raw data. Real data experiments guarantee the effectiveness of our proposed scheme.

When the range migration caused by motion error exceeds the range cell resolution, the performance of a conventional phase autofocus approach degrades. In this paper, a new adaptive motion compensation (MoCo) algorithm with the removal of the migration that is nonsystematic has been developed for airborne spotlight synthetic aperture radar (SAR) imagery with high resolution. In the algorithm, the relationship between nonsystematic range cell migration (NsRCM) and phase error was first explicitly revealed after the polar format algorithm resampling. The NsRCM could be readily calculated by coarse but reliable phase error estimation. Subsequently, the NsRCM and the bulk of the azimuth phase error were corrected. After the removal of the NsRCM, degradation of the conventional phase autofocus resulting from sidelobe increase as well as mainlobe broadening was avoided. Finally, a fine MoCo procedure was performed to remove the residual azimuth phase error satisfactorily. Through the analysis of the airborne spotlight SAR raw data with high-resolution and wide-swath illumination, a well-focused imagery was obtained. Quantitative assessment of the image quality was satisfactory. The MoCo algorithm was validated.

In this letter, we discuss the problem that linear range cell walk correction in the azimuth time domain may cause space variation along the azimuth not only to the quadratic phase but also to the quadratic range cell migration (QRCM) under the conditions of high resolution and large scene along the azimuth. Moreover, an algorithm is proposed to deal with this problem. The proposed algorithm adopts the azimuth space variation filtering in the range frequency domain. In addition, the range-dependence component of QRCM is corrected by linear chirp scaling, and the unified QRCM can be corrected in the 2-D frequency domain. The proposed algorithm, without interpolation, can be easily implemented by integrating with motion compensation for image processing. Simulation and airborne strip-map real data show the accuracy and efficiency of the proposed algorithm.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.

For highly squinted synthetic aperture radar (SAR) imaging, the wavenumber domain SAR processing algorithm is commonly accepted as an ideal solution to SAR focusing in the case of an ideal straight sensor trajectory. However, airborne SAR is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, we propose a robust autofocusing approach for highly squinted airborne SAR imagery using the extended wavenumber algorithm, being capable of estimating the range-dependent phase errors. To apply the proposed autofocusing scheme, a detailed analysis of the motion error model in the conical reference system is presented, where the formulation of range-dependent phase errors for squinted SAR is given. The proposed autofocusing approach is performed by a three-step process: referring to the inevitable residual phase after deramping for highly squinted SAR, a modified squinted phase gradient autofocusing (SPGA) algorithm is put forward to retrieve the range-independent phase errors; based on the established motion error model, the residual range-dependent phase errors are estimated using a local maximum likelihood-weighted SPGA algorithm; and motion compensation is executed by a two-step approach to reach the range-independent and range-dependent corrections, respectively. Experiments based on measured data have shown that the proposed autofocusing approach performs well for highly squinted SAR imaging.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

In this letter, a new strategy based on nonnegative matrix factorization (NMF) is proposed for polarimetric ship detection. This method utilizes the sparse feature of nonnegative eigenvalues, and the sparse degree is proposed to be estimated from the histogram which can reveal the sparse distribution of eigenvalues. Combining the nonnegative and sparse features, the NMF-based ship detection method can be implemented flexibly and efficiently. It has been carried out on the C-band quad polarimetric synthetic aperture radar (PolSAR) and dual PolSAR ocean data sets to validate its effectiveness. Unlike a constant-false-alarm-rate detector, the NMF method does not depend on target size and therefore offers improved detection performance under low-signal-to-clutter-ratio conditions.

In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

The goal of this paper is to suppress the narrowband interference (NBI) and wideband interference (WBI) in synthetic aperture radar (SAR) by using a nonparametric method. The method is based on the analysis of time-frequency characteristic of NBI and WBI from which an interference suppression filter combined with the constant false alarm rate algorithm is designed. In this approach, the short-time Fourier transform (STFT) is used to estimate the instantaneous frequency of the SAR echo data with interference. In the STFT domain, the instantaneous frequency spectrum is represented by wavelet, and then, the designed filter filters the corresponding wavelet coefficients of the interference components. In addition, this algorithm is robust to time-varying NBI and WBI. The performance of the proposed approach is evaluated by the simulated and measured data, and the effectiveness is demonstrated.

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

In this letter, a 3-D inversed synthetic aperture radar imaging algorithm for targets in high-speed spinning is proposed based on 2-D spectrum matched filter (MF) banks. Each spectrum MF bank yields a focused slice for its corresponding scatterers. By extracting the spatial parameters from all slices, the 3-D image of the target can be constructed. Numeric simulation confirms the validity of the algorithm.

Recent theory of compressed sampling (CS) suggests that exact recovery of an unknown sparse signal with overwhelming probability can be achieved from very limited number of samples. In this letter, we adapt this idea and present a framework of high-resolution inverse synthetic aperture radar imaging with limited measured data. During the framework, we mathematically convert the imaging into a problem of signal reconstruction with orthogonal basis; hence, a conceptive upper bound of the cross-range resolution is presented based on the CS theory. Real data results show that the CS imaging approach outperforms the conventional range-Doppler one in resolution.