Micro-Doppler Analysis (MDA) of rigid-body targets is crucial for various practical downstream tasks such as target imaging and recognition. Radar echoes from micro-moving targets typically represent non-stationary signals and are often described using the parameterized Time-Varying Auto Regressive (TVAR) model. Sparse Bayesian Learning (SBL) is commonly employed to estimate time-invariant coefficients, thereby achieving high-resolution micro-Doppler time-frequency distributions. Despite its effectiveness, SBL-based optimization methods often face inefficiency due to the computational burden of inverse operation. To address this challenge and enhance the efficiency of MDA based on TVAR model, we propose a Sparse Bayesian Network (SBN) that unfolds a fast Mean Field SBL (MF) using a deep variational autoencoding framework. This method retains the optimization effectiveness of SBL while incorporating the inference efficiency of Deep Neural Networks (DNNs). Our proposed method demonstrates strong generalization capabilities, performing well on both simulated and measured radar echoes.

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

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

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

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

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

Few-Shot Industrial Anomaly Detection (FS-IAD) has drawn great attention most recently since data efficiency and the ability to design algorithms for fast migration across products have become the main concerns. The difficulty of memory-based IAD in low-data regime primarily lies in inefficient measurement between the memory bank and query images. We address such a pivotal issue from a new perspective of optimal matching between features of image regions. Taking the unbalanced nature of query features into consideration, we adopt Conditional Transport (CT) as a metric to compute the structural distance between representations of the two sets to determine feature relevance. CT distance generates the optimal matching flows between unbalanced structural elements that achieve the minimum matching cost, which can be directly used for IAD since it well reflects the differences of query images compared with the normal memory. Realizing the fact that query images usually come one-by-one or batch-by-batch, we further propose an Online Conditional Transport (OCT) by making full use of the current and historical query images for IAD via simultaneously calibrating the memory bank using the online query images and matching features between the calibrated memory and the current query image. Go one step further, for sparse foreground products, we employ a predominant segment model to implement Foreground-aware OCT (FOCT) for improving the effectiveness and efficiency of OCT by forcing the model to pay more attention to diverse targets rather than redundant backgrounds when calibrating the memory bank. FOCT can improve the diversity of calibrated memory, which is critical for robust FS-IAD in practice. Besides, FOCT is flexible since it can be friendly plugged and played with any pre-trained backbones, such as WRN, and any pre-trained segment models, such as SAM. The effectiveness and efficiency of our model is demonstrated across diverse datasets, including benchmarks of MVTec and MPDD, achieving SOTA performance.

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

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

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

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

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

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

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