The rapid development of drone technology has spurred significant interest in multi-UAV collaborative systems, particularly for complex tasks like cooperative target jamming. However, realizing their full potential is hindered by significant challenges, primarily stemming from uncertain three-dimensional (3-D) target positions and operational time constraints. These factors complicate crucial aspects like path planning and efficient task allocation, ultimately jeopardizing jamming mission success. Furthermore, the specific complexities introduced by uncertain 3-D target positions are often overlooked in existing cooperative jamming strategies. To address these issues, we propose cooperative multi-agent jamming techniques using reinforcement learning (RL) to maximize interference effectiveness against designated targets under target position uncertainty. Our methodology is based on a task framework that unifies the models of target position uncertainty, 3-D probabilistic perception for high-fidelity UAV sensing, and directional antenna interference to achieve optimal jamming. Within this framework, we formalize the task as a Markov Decision Process (MDP) and employ reinforcement learning to optimize collaborative jamming policies under target positions uncertainty. The proposed RL algorithm, by utilizing both individual and collaborator rewards, adaptively balances exploration and exploitation across different mission stages. This balance is achieved by adjusting the amplitude of noise used for action selection. We conducted simulation experiments with various UAV, target and no-fly zone configurations to validate the effectiveness of our proposed method, demonstrating its scalability and strong joint task performance in achieving jamming objectives.

Recently, video synthetic aperture radar (video SAR) has drawn widespread attention due to its capability to monitor moving targets continuously. Tracking the moving targets in video SAR using the shadow information has been proven as a more effective method. However, the existing tracking methods process each target independently and ignore the interframe interactions. To deal with this issue and improve the tracking performance, we propose a graph feature representation algorithm for video SAR multitarget tracking (MTT) using the global topological information. Specifically, a directed graph is built for each detected shadow based on the neighbor spatial relations, where each node is the semantic features of the corresponding shadow and each edge is the relative position features with neighboring shadows. Subsequently, the detected shadows are associated with the tracking shadows according to the similarity of their graphs to achieve moving target tracking. Experimental results on the video SAR dataset validate that compared with the state-of-the-art (SOTA) tracking algorithms, our algorithm has higher tracking accuracy and lower identity (ID) switching rate.

The aerial targets with maneuvering trajectories can result in severe defocusing in inverse synthetic aperture radar (ISAR) images, while the low signal-to-noise ratio (SNR) from remote observations can also pose challenges to imaging. However, due to the stable motion state of satellites and their higher speeds compared to aerial targets, the accumulation time required for imaging is relatively short. This makes the impact of aerial target trajectory maneuvers on imaging effects relatively minor, providing a significant advantage for robust imaging of aerial targets with maneuvering trajectory under a spaceborne SAR/ISAR hybrid mode. This letter proposes an algorithm for robust imaging of aerial targets with maneuvering trajectory based on multioptimization strategies (OSs) under a spaceborne SAR/ISAR hybrid mode. In this algorithm, robust imaging is divided into two parts: 1) multistep optimal imaging time interval selection method based on OSs (MS-OITI-OSs). Through multiple optimization steps based on the target’s aerial trajectory and image information, the method achieves robust selection of the OITI by utilizing an “attitude first, quality later” optimization strategy, reducing computational complexity and increasing imaging success rates; and 2) joint translational motion compensation method based on OSs (JTMC-OSs). Characterizing motion parameters using a polynomial model, the method optimizes the motion parameters using image entropy as the objective function through the Grasshopper optimization algorithm (GOA). During the optimization process, a “high-order first, low-order later” optimization strategy is employed based on the impact of motion parameters on imaging quality to achieve robust translational motion compensation. The proposed algorithm enables robust imaging of aerial targets with maneuvering trajectory based on multi-OSs under a spaceborne SAR/ISAR hybrid mode. Extensive experimental validation confirms the effectiveness and robustness of the proposed method.

For high-resolution squinted airborne synthetic aperture radar (SAR) imaging, both linear range walk correction (LRWC) and motion error introduce significant azimuth spatial-variant (ASV) characteristics in the radar echo, rendering the classical assumption of "azimuth translational invariance" no longer valid. Existing sub-aperture methods attempt to overcome the ASV characteristics of the signal by performing segmentation processing in the data domain or the image domain. However, grating lobes or image stitching problems inevitably occur in the focused images. Existing full-aperture methods, on the other hand, utilize azimuth resampling or nonlinear chirp scaling (NCS) to address the ASV problem. Nevertheless, the above-mentioned methods basically handle the ASV characteristics introduced by LRWC and motion errors separately, without considering the coupling characteristics between the two. Therefore, this paper proposes a high phase-preservation squint airborne SAR autofocus imaging method by modifying the traditional azimuth resampling processing, so that only a single azimuth resampling factor is required to simultaneously solve the ASV problems brought about by LRWC and motion errors. The imaging processing results of airborne squint SAR real-data verify its good focusing effect. Meanwhile, the interferometric processing results of multi-pass cross-track SAR real-data also indicate that the proposed algorithm exhibits a high phase-preservation capacity. The images processed by the proposed algorithm and the comparison algorithms, as well as the multi-pass cross-track SAR complex images after registration, can be downloaded from https://pan.baidu.com/s/1okgAkp18ynK7qzKXe2lceQ?pwd=nquf.

Existing deep learning-based methods for synthetic aperture radar (SAR) target recognition typically rely solely on the amplitude images without considering the complex characteristic of SAR images, making it difficult to recognize SAR targets with high visual similarity. To solve this issue, a novel dual-stream manifold multiscale network fused with electromagnetic features, i.e., EFMM-Net is proposed for target recognition in complex-valued SAR images. In EFMM-Net, the attributed scattering center (ASC) model is first utilized to reconstruct the complex-valued SAR image, thereby highlighting the electromagnetic scattering features of the target. Subsequently, the reconstructed complex-valued SAR image is combined with the original one to construct the dual-stream input. Second, a scattering-guided manifold multiscale (SGMM) backbone is proposed for parallel extraction of data features and electromagnetic scattering features of the target from the dual-stream input. During feature extraction, the SGMM backbone can effectively leverage the phase information of complex-valued SAR images and inject target scattering information into data features through scattering-guided channel-wise feature alignment, thus enhancing the awareness of data features to critical scattering characteristics. Finally, to effective fuse the data features and electromagnetic scattering features, a location awareness feature fusion (LAFF) recognition module is proposed. By exploiting coordinate attention, LAFF utilizes the target location information captured from electromagnetic scattering features to direct the feature fusion process, thereby increasing the focus of fusion features on the target region. The extensive recognition results of three-class and six-class ship targets in the OpenSARShip 2.0 dataset demonstrate the effectiveness and superiority of the proposed method.

High-resolution radar imaging with compressive sensing (CS) is significantly important and meaningful in practical applications, such as data collection burden reduction and resource allocation scheduling in a multifunctional radar. The class of matrix completion (MC) methods is a powerful tool to directly reconstruct the missing data to be applied in sparse radar imaging, which can overcome the discrete error drawback of traditional dictionary-based CS methods. In this article, we extend the MC method to tensor completion (TC) with multidimensional data representation, and a novel manifold low-rank and sparse TC (MLRSTC) radar imaging algorithm is proposed for enhanced sparse imaging performance. In the scheme, an attractive tensor radar data model is proposed, and the low-rank tensor property is discovered by capturing the latent and intrinsic data structure in high dimensions. In particular, the low-rankness superiority of the tensor model is confirmed by both the theoretical derivation and experimental analysis. Then, the Kronecker-basis-representation (KBR)-based tensor sparsity model is applied to format the proposed MLRSTC algorithm of sparse radar imaging, which can effectively promote the reconstruction of tensor data with enhanced low-rank property. Meaningfully, the proposed MLRSTC algorithm can work well under the condition of different sparse data sampling patterns. Next, the proposed MLRSTC algorithm is efficiently solved in an iterative manner under the framework of alternating direction method of multipliers (ADMMs) by updating the involved parameters in a closed-form solution. Finally, the experiments using both electromagnetic simulation and measured data are performed to confirm the effectiveness and superiority of the proposed MLRSTC algorithm beyond state-of-the-art (SOTA).

Lack of full use of phase information is a common problem in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we propose a complex manifold multi-scale feature fusion network (CMMFF-Net) for SAR image target recognition. Unlike traditional complex-valued networks, we extend SAR complex images to complex manifold space and construct a complex-valued manifold feature extraction module, which can extract manifold features from SAR amplitude and phase images. Moreover, the multiscale feature extraction and fusion module helps to further extract richer and discriminative target features by fusing multiscale information. Experimental results on SAR complex image dataset demonstrate the effectiveness of proposed method.

Automotive synthetic aperture radar (SAR) that can achieve long aperture by coherently processing chirps collected by radar mounted on moving vehicle platform shows remarkable superiority in terms of angular/azimuth resolution. To further enhance imaging performance, MIMO technology has been combined with SAR for extended signal-to-noise ratio (SNR), side-lobe level and etc. In this paper, an automotive MIMO SAR image fusion algorithm using tensor decomposition is proposed. In the scheme, the redundancy between MIMO SAR image stacks after compensating phase difference between channels is modeled as the low-rank property of tensor. Then, the low-rank tensor representation is verified and adopted to enhance the image quality of MIMO SAR imaging. Numerical experiments using measured data from an automotive MIMO radar system are carried out. The imaging results obtained using the proposed algorithm show significant improvement compared to single channel SAR and MIMO digital beamforming (DBF) results.

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.

The microwave photonic synthetic aperture radar (SAR) is capable of realizing large scene remote sensing observation with centimeter or even millimeter resolution, which greatly enhances the ability to acquire target information. A key issue in airborne microwave photonic SAR imaging is how to accurately correct the two-dimensional (2-D) spatial variation characteristic of the motion error. The typical two-step motion compensation (MoCo) method cannot correct the azimuth spatial variant characteristic of motion error, and the traditional subaperture methods may introduce the problems of grating lobes and image stitching. In addition, the efficiency of existing parametric full-aperture autofocus methods is usually low. To solve the above problems, a nonparametric full-aperture autofocus method based on a two-stage processing framework is proposed in this article. The first stage is to introduce a nonparametric low-order nonlinear chirp scaling (NCS) or resampling (RS) model to compensate for the low-order spatial variant motion error that accounts for the dominant component before the range cell migration correction (RCMC), which ensures that there is no significant residual RCM after the RCMC. The second stage introduces a nonparametric high-order NCS/RS model after the RCMC to compensate for the remaining high-order azimuth spatial variant phase error to achieve accurate azimuth focusing. Based on the full-aperture processing strategy, the algorithm proposed in this article avoids the problems existing in the subaperture methods. In addition, the nonparametric modeling is used throughout the autofocus processing (e.g., motion error estimation and parameter reversion of NCS/RS model), which greatly improves the efficiency of autofocus processing. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

Interferometric synthetic aperture radar (InSAR) images are severely corrupted by noise in both magnitude and phase. It is significantly essential to recover the true interferometric phase during InSAR signal processing. Usually, traditional phase denoising methods are to find homogeneous samples for filtering with the need to balance noise reduction and phase preservation, which may be a problem in dealing with topography scenes. In this article, a novel algorithm of robust nonlocal tensor decomposition (RNLTD) for InSAR phase denoising is proposed. In the scheme, a nonlocal tensor (NLT) model of the interferogram is constructed by selecting and stacking similar image patches in a nonlocal region. Benefiting from the simultaneous use of nonlocal and tensor tools, superior low-rank properties of this NLT can be acquired, which is also confirmed by numerical analysis. Then, a robust tensor decomposition algorithm is proposed to formulate the low-rank recovery of the interferogram and constrain the sparse outliers for noise reduction. Next, an alternating direction method of multipliers (ADMM) solution is applied to robustly and accurately restore the noise-reduced interferometric phase. As a result, the proposed RNLTD algorithm takes advantage of effectively capturing the phase structure in a high-dimension manner, which is helpful in phase preservation with the achievement of excellent noise reduction. Lastly, the experimental analysis using one set of simulated and two sets of measured InSAR data is performed to show the promising performance of the proposed algorithm.

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.

In this paper, we investigate the trade-off between positioning and communication for an integrated positioning and communication (IPAC) millimeter wave system. First, in terms of the positioning in the IPAC system, the Cramér-Rao bound (CRB) is commonly used as a performance metric. Unfortunately, the CRB is only tight in a certain region for the high signal-to-noise ratio (SNR). To compensate for this deficiency, we derive the Ziv-Zakai bound (ZZB) for the IPAC system by exploiting the a priori delay information extracted from both the time delay parameter and the channel amplitude attenuation. Further, we derive the expected CRB (ECRB) and the weighted CRB (WCRB) of the system for comparisons. Second, we analyze the trade-off between positioning and communication for this IPAC system based on the derived ZZB. Specifically, we aim to optimize the power allocation to maximize the achievable data rate subject to the ZZB and total transmit power constraints. Numerical results show that the ZZB provides a tighter and more reasonable bound for the minimum mean square error (MMSE) estimator over the wide range of SNRs compared to the ECRB and WCRB. Moreover, the trade-off between positioning and communication is revealed via changing critical parameters by simulations.

Tomographic synthetic aperture radar (TomoSAR), which can provide three-dimensional (3-D) image of the observed scenes, has become an important technology for topographic mapping, forest parameter estimation, urban buildings modeling and etc. Recently, the developed compressed sensing (CS) and other similar methods have been widely applied for the achievement of super-resolution SAR tomography. However, there always exists inevitable model errors during the mining of scene information, such as discrete gridding on used dictionary and outliers among independent identically distribution (IID) samples, which tends to dramatically degrade the TomoSAR inversion. In this paper, a novel robust gridless CS (RGLCS) algorithm is proposed for high-resolution 3-D imaging of array TomoSAR. In the scheme, the atomic norm minimization (ANM) is used to model the joint-sparsity pattern on elevation distribution between adjacent pixels, which can be treated as gridless CS to avoid the discrete error of the dictionary. Meanwhile, the outliers and disturbances not satisfying the IID elevation distribution are modelled as sparsely distributed spike-noise in the image domain. The proposed RGLCS algorithm has the capability of perfectly separating the outliers and maintaining high-precision height resolution. For efficient solution, a fast alternative optimization is used to solve the objective function to effectively reduce the computational complexity. Next, the post-processing, including point cloud clustering and double-bounce scattering detection & eliminating, are studied to obtain high-resolution 3-D point cloud image. Finally, the experimental analysis using both simulated and measured data are performed to verify the effectiveness of the proposed algorithm. In particular, a practical demonstration using measured airborne array TomoSAR data is presented for urban mapping.

There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.

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.

For multi-channel synthetic aperture radar (SAR), the high-coherence between different channel images provide low-rank property. Meanwhile, the ground moving target (GMT) exhibit sparse feature in the image domain. As a result, it is possible to apply robust principal component analysis (RPCA) method for enhanced performance of SAR ground moving target indication (SAR GMTI). In this paper, a joint multi-channel sparsity approach of RPCA is proposed for SAR GMTI by improving the performances of clutter suppression and GMTI. The joint sparsity feature between multi-channel images is modelled from the coherence between multi-channel data, enhancing the sparse signature of moving targets. Compared with the independent-channel sparse approach, the proposed joint sparsity approach is more robust to clutter or noise and has better performance in low signal-to-clutter/noise-ratio (SCNR) by persevering the moving targets. Finally, experimental analysis is implemented to confirm the effectiveness of the proposed algorithm.

Cross-range scaling in inverse synthetic aperture radar (ISAR) systems is crucial to target recognition. However, it is still challenging for non-uniformly rotating targets due to the time-varying Doppler history. This paper presents a method to estimate the scaling factor by optimizing the image quality. One highlight of this work is the usage of the Matching Fourier Transform (MFT), which coincides with the non-uniform rotation model. Another lies in the sharpness maximization of the MFT image by iteratively compensating the range-dependent phase error joint with the image formation. The proposed method manages to achieve the scaling factor as well as the enhanced images. Simulation and real-data results confirm the effectiveness of the proposed method.

For multi-channel synthetic aperture radar of ground moving target imaging (SAR GMTIm), the moving targets in SAR image domain are sparse after clutter suppression, which provides the possibility of using sparse approach. In this paper, a reweighted sparse algorithm of SAR GMTIm is proposed to improve the imaging performance. Intuitively, both the magnitude and interferometric phase can exhibit the moving target signatures by applying displaced phase center antenna (DPCA) technique and along-track interferometry (ATI), respectively. So a hybrid metric of magnitude and interferometric phase is constructed to be as the weights of the reweighted sparse approach. Compared with the unweighted approach, the proposed reweighed approach can effectively improve the sparse imaging performance. Finally, experiments using measured data are performed to confirm the effectiveness of the proposed algorithm.

For a single-channel synthetic aperture radar (SAR), finite-pulse repetition frequency and nonideal antenna pattern cause azimuth ambiguities, i.e., ghosts in image domain. In this paper, a novel algorithm of locating processing weighted group lasso SAR image formation for maritime targets is proposed to effectively mitigate the ambiguities, which can work on a single-look complex SAR image. In the scheme, the ambiguous signal model using the conventional SAR focusing processor is first explicitly derived, showing the analytical expression of SAR image formulation. The weighted sparse group lasso algorithm is then employed to group-sparsely reconstruct the subimages of nonambiguous and ambiguous Doppler components. In particular, we introduce adaptively weighted sparsity constraint, obtained from a priori azimuth antenna pattern, and clutter clustering during sparse imaging. It should be emphasized that the proposed algorithm can effectively improve the azimuth resolution by coherently integrating the ambiguous signal components, which greatly helps the target detection and recognition in maritime surveillance. Finally, experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Recently, sparse radar imaging has drawn more and more attentions, which has the superiority of feature enhancement, super-resolution and so on. In this paper, we focus on sparse moving target imaging (MTIm) using a SAR sensor from sparse aperture (SA) data. For maneuvering targets, their strong motion tends to introduce migration through range cell (MTRC), which increases the difficulty of SA imaging. In this paper, a novel algorithm of moving target imaging is proposed to deal with both the MTRC and SA. In the scheme, a scaled Fourier dictionary is employed to represent the MTRC. A hierarchical model of statistics is used to encode the sparsity of image. Then, SA imaging is treated as a problem of sparse Bayesian learning, which is solved by expectation maximization (EM) method. The scaled Fourier dictionary is modified to resolve the velocity ambiguity. Finally, experimental analysis is performed to confirm the effectiveness of the proposed method.

Recently, compressive sensing theory has been successfully applied in inverse synthetic aperture radar (ISAR) imaging. However, the issue of maneuvering target imaging from compressive sampling data has not been sufficiently addressed because it is difficult to jointly deal with both sparse imaging and motion compensation under compressive sampling. In this paper, we develop a novel algorithm of high-resolution ISAR imaging for maneuvering targets from compressive sampling data. In this algorithm, a non-uniform scaled Fourier dictionary is constructed to represent the maneuverability. A hierarchical statistical model is utilized to encode the sparsity of ISAR image. Then, ISAR imaging joint with motion estimation is solved by using a parametric sparse Bayesian leaning (P-SBL) method, including sparse imaging and dictionary learning. Finally, experiments are performed to confirm the effectiveness of the proposed method by using the simulated and measured data.

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.

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.

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.

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.