Automotive synthetic aperture radar (Auto-SAR) imaging can provide high-resolution images for vehicular environment perception and localization. Due to the severe motion errors associated with jerks and bumps of the vehicle, autofocusing plays a crucial role in Auto-SAR imaging. This paper aims to provide an integral Auto-SAR imaging workflow via the Omega-K algorithm and a contrast-based WPGA (CB-WPGA) method. Based on the analysis of phase error characteristics after sub-aperture (SA) imaging, CB-WPGA, which selects range cells and weights the PGA kernel based on the contrast, is proposed to enhance the robustness of PGA in automotive scenarios. Finally, the superiority of the proposed technique is showcased by employing experimental data obtained from a 77-GHz radar installed on a vehicle.

Identifying key nodes in a network is crucial for practical applications, especially when it comes to accurately detecting cut vertices, which play a vital role in maintaining network stability. As network complexity increases, relying on a single method to identify key nodes often fails to provide a comprehensive assessment of node importance in real-world scenarios. To address this, this paper proposes a novel framework for cut vertex identification that evaluates node importance from three perspectives: weighted fusion centrality metrics, the spanning tree algorithm, and topology graph properties. Additionally, existing methods based on centrality metrics often struggle with low accuracy in identifying cut vertices. To overcome this limitation, we have devised a method, termed the MIS-CV algorithm, that enables the accurate identification of network cut vertices by solely evaluating the intersection of node neighborhoods within the maximum independent set. We validated our proposed algorithm through simulation analysis on the Barabasi-Albert scale-free network. The results demonstrate that the MIS-CV key nodes identification algorithm surpasses traditional search tree algorithms in terms of both accuracy and operational efficiency. This proves that the MIS-CV algorithm has higher recognition accuracy without increasing computational complexity.

This paper explores the waveform design problem for multiple-input multiple-output radar, focusing on minimizing the mean square error between the idealized beampattern and its actual implementation. The optimization respects two practical non-convex constraints: constant modulus and similarity. To address this non-convex optimization challenge, we propose a solution based on the Riemannian Product Manifold Conjugate Gradient (RPM-CG) framework. The RPM-CG framework transforms the multi-constrained non-convex problem in Euclidean space into a straightforward unconstrained one on the product manifold. Specifically, we leverage the conjugate gradient method to perform a descent search on the developed product manifold. Simulations demonstrate that RPM-CG outperforms other candidate beampattern design methods, enhancing the matching performance of the beampattern, reducing computational complexity, and maintaining waveform ambiguity properties.

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task predictor, where the encoder extracts spectrum usage pattern features, and the predictor configures different networks according to the task requirements to predict future spectrum. Based on the DeepSPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i.e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB. 3D-SwinSTB unique3D Patch Merging ViT-to-3D ViT Patch Expandingand pyramid designs help the model accurately learn the potential correlation of the evolution of the spectrogram over time. Then, we propose a novel spectrum occupancy rate (SOR) method by redesigning a predictor consisting exclusively of 3D convolutional and linear layers to serve possible applications such as dynamic spectrum access (DSA) task, named 3D-SwinLinear. Unlike the 3D-SwinSTB output spectrogram, 3D-SwinLinear projects the spectrogram directly as the SOR. Finally, we employ transfer learning (TL) to ensure the applicability of our two methods to diverse spectrum services. The results show that our 3D-SwinSTB outperforms recent benchmarks by more than 5%, while our 3D-SwinLinear achieves a 90% accuracy, with a performance improvement exceeding 10%.

In this paper, we explore the joint design of a transmit waveform and receive filter to enhance the detection performance of multiple-input multiple-output (MIMO) radar. Target echoes are assumed to be embedded in signal-dependent interference and colored Gaussian noise. As design metrics, we exploit two information-theoretic criteria, including mutual information (MI) and relative entropy. The joint design problems of MIMO radar associated with different information- theoretic criteria are established as a unified optimization framework within a constant-envelope (CE) constraint. We propose an efficient method based on the Riemannian optimization framework, which transforms the constraint optimization problems into unconstrained problems by leveraging the geometry of the feasible region. Several numerical examples are included to demonstrate the effectiveness of the proposed method.

As a wideband radar system, synthetic aperture radar (SAR) may conflict with several electromagnetic systems. These signals may severely interfere with SAR image quality. Numerous previous researches focused on the interference suppression problem, among which semiparametric methods, such as low-rank recovery methods, have been verified to have state-of-the-art (SOTA) performance. However, semiparametric methods are restricted by extremely strong interferences when the signal-to-interference-and-noise ratio (SINR) exceeds the ability upper bound of semiparametric methods. In recent years, multichannel SAR (MC-SAR) systems have been widely used for more applications, where multiple antennas are mounted along the azimuth or in elevation. Adaptive digital beamforming (DBF) is a classic spatial filtering method to focus energy in the expected direction and suppress unexpected interferences. Its performance is determined by the array manifold and the interference-impinging angle. In this article, we propose a novel space-time-combined method that takes advantage of both low-rank recovery methods in the 2-D time domain and the adaptive DBF method in the spatial domain. Specifically, we construct a single optimization problem to unify both kinds of methods. The alternating direction of the multiple multiplier (ADMM) framework is leveraged with a closed-form solution for each step. Multiple experiments are provided to demonstrate the effectiveness of the proposed method.

For a spaceborne or an airborne synthetic aperture radar (SAR) system, it may be easily interfered with radio frequency interferences (RFIs) due to the increase demand for frequency occupation. The RFIs introduce artifacts in the focused SAR imagery, resulting in high noise floor and erroneous interpretations. Previous works on narrowband RFI mitigation mainly focused on single-channel SAR systems, while with the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multi-channel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). In this paper, we propose a novel interference-mitigation model and analyze the low-rank property of the narrowband RFI for HRWS SAR systems. Then, we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the RFIs by solving the low-rank recovery problems of RFIs. The real airborne SAR data is used to demonstrate the effectiveness of the proposed method.

In the above article[1], the corresponding authors should be Yan Huang and Jie Li.

As a wideband radar system, the synthetic aperture radar (SAR) usually conflicts with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, which are radio frequency interference (RFI) for radar systems, severely interfere with SAR systems to generate a high-resolution image. Some previous parametric methods focused on the time-varying RFI model; however, they cannot realize the comparable effectiveness and efficiency against semi-parametric methods. However, previous semiparametric methods did not focus on the time-varying RFI case. Hence, in this letter, a graph Laplacian clustering (GLC) semiparametric algorithm is proposed to suppress RFIs by constructing the Laplacian embedding connections between different pulses of signals. As a result, locally time-varying interferences are clustered in a nonlinear low-dimensional manifold and can be effectively mitigated. The real SAR data with measured RFIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available athttps://github.com/huizhangyang/BSF.

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

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.

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.

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

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.

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.

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

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

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

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.

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

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

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

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

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

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

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