Sparse aperture inverse synthetic aperture radar (SA-ISAR) aims to reconstruct target images by undersampled data. Traditional algorithms are limited in their application scope and exhibit weak capabilities in reconstructing target details. To address these issues, an adaptive threshold sensing (ATS) sparse reconstruction algorithm based on alternating direction method of multiplier (ADMM), named ATS-ADMM, is proposed. Within our framework, a detail-enhancing operator (DEO) is designed and combined with the$l_{1}$-norm to form a joint constrained optimization function to facilitate the recovery of weak scatterers. The matrix inversion operation within the ADMM framework is optimized to efficiently solve the multiconstrained problem. To enhance clutter suppression, an adaptive threshold network is designed based on deep convolutional networks. Inspired by deep learning, the DEO is set as a learnable operator, and the parameters are trained using an unsupervised network. Finally, the performance of ATS-ADMM is validated by comparing it with advanced algorithms using both simulated and real data. The results demonstrate that ATS-ADMM effectively focuses images, is suitable for diverse imaging scenarios, and is the fastest among ADMM-based algorithms.

Non-line-of-sight (NLOS) moving target imaging is an emerging and challenging technology with potential applications in autonomous driving, security detection, disaster response, and more. In this article, a novel algorithm dubbed NLOS detail recovery via alternating direction method of multipliers (NDR-ADMMs) is proposed for NLOS moving target imaging. In our scheme, the static clutter filter (SCF) we proposed is utilized for NLOS clutter suppression, which facilitates hidden motion target echo extraction. To address the sparsity of echoes caused by scene complexity and target motion, we introduce a regularization constraint termed detail-aware regularization (DAR), which enhances details and suppresses noise in NLOS scenes by incorporating information from neighboring cells and expanding the receptive field of the image. Then, we propose the NDR-ADMM that combines DAR,$\ell _{1}$-norm, and ADMM to reconstruct high-resolution NLOS moving target images. Further, the corresponding fast version, NDR-ADMM+, is derived by mapping the NDR-ADMM to the adaptive parameter learning network for improving robustness and convergence. Finally, the proposed NDR-ADMM and NDR-ADMM+ are verified by simulated data and measured data we collected via millimeter-wave (MMW) radar in various NLOS scenarios. Compared to other state-of-the-art methods, NDR-ADMM+ demonstrates superior performance, robustness, and noise immunity, with NDR-ADMM following closely behind. This is attributed to DAR’s ability to capture details and suppress interference. Additionally, NDR-ADMM+ and AF-AMPnet offer the fastest processing speeds.

The detection of obscured vehicle targets and non-line-of-sight imaging by vehicle-mounted radar systems have broad application prospects in the field of urban traffic and autonomous driving. In this paper, a non-line-of-sight (NLOS) model and synthetic aperture radar (SAR) imaging method are proposed to perform millimeter wave imaging of obscured vehicle targets using electromagnetic wave reflection echoes from the road surface. Then, the NLOS echoes are imaged in two dimensions with high accuracy by compressed sensing algorithm (CSA). Finally, an experimental system for NLOS vehicle targets was developed using TI millimeter-wave radar. The feasibility of millimeter-wave NLOS radar imaging and the effectiveness of the proposed algorithm are experimentally verified, and high-precision 2D imaging results of obscured vehicle targets are obtained.

Tomographic SAR (TomoSAR) can achieve 3-D imaging for observation targets through tomographic synthetic aperture, and shows good characteristics in urban building information extraction and scene 3-D inversion. Though the existing CS-based imaging algorithms can achieve high-resolution imaging results, requiring multiple iterations and manual adjustment of hyper-parameters. Currently, deep learning techniques in TomoSAR show great advantages in improving the imaging accuracy and efficiency. Inspired by deep unfolding, we unfolded the CS-based ADMM algorithm and mapped it into deep unfolded ADMM-net (DUADMM-net), so as to achieve high-resolution TomoSAR imaging. DUADMM-net consists of reconstructed signal estimation module, nonlinear fitting module and multiplier update module. The introduction of convolutional layers enhances the learning ability and nonlinear fitting ability. Compared to the conventional sparse imaging algorithms, the experimental imaging results and quantitative indicators demonstrate the effectiveness and efficiency of DUADMM-net.

Non-line-of-sight (NLOS) imaging of moving targets is of tremendous interest in the fields of urban sensing and autonomous driving. In this paper, a novel NLOS inverse synthetic aperture radar (ISAR) imaging method is proposed for moving targets by automotive millimeter-wave (MMW). In this scheme, an imaging model of the moving target in urban scenes is developed and analyzed. A low-frequency filtering method is employed to remove stationary interfering signals, a typical threshold method is applied to extract the triple-reflected echo of a hidden moving target, and the range migration algorithm (RMA) is utilized to achieve envelope alignment. Then, the well-focused image of the moving target is achieved by the polar format algorithm (PFA) with Prominent Point Processing (PPP) autofocus algorithm. Finally, an outfield experimental system for the obscured moving targets is built by TI MMW sensors. The results demonstrate our method can provide a high-resolution image of the moving target.

Inverse Synthetic Aperture Radar (ISAR) target recognition is an important branch of ISAR image research. The traditional ISAR recognition mission is done based on monostatic radar. However, the monostatic ISAR can only generate a single view image of the target. In this paper, to improve the recognition accuracy, a method of joint target recognition for Multi-station ISAR (MS-ISAR) via Multi-station ISAR Image Recognition (MIIR) network is proposed. In this scheme, the spatial matching algorithm and SURF algorithm are exploited to achieve multi-view fusion. The MIIR is present to achieve high accuracy recognition. To validate the proposed method, we use electromagnetic simulation software to obtain multi-view echo data of six types of aircraft targets. Then the proposed method and the traditional method are used for recognition respectively. Finally, we acquire the real experiment data of a model aircraft to validate the effectiveness of the proposed method. The results demonstrate our method provides a higher recognition accuracy rate.

The wide application of automotive radar greatly increases the risk of mutual interference between vehicles. To address this problem, this paper proposes an efficient interference suppression framework based on frequency domain sparsity. Firstly, The linear time-domain signal model is transformed into an optimal solution to the problem of extracting targets. Moreover, we utilize the orthogonal property of the Fourier matrix to avoid complex inverse matrix calculations and greatly reduce the computational memory while maintaining interference suppression performance. Both simulation and measured data validate the effectiveness of our approach, showing that our method not only suppresses mutual interference between automotive radars but also extracts range information from multiple targets.

High-resolution imaging of the corner-hidden moving target makes tremendous sense in the fields of urban sensing and autonomous driving. In this paper, a joint No-line-of-sight (NLOS) model and inverse synthetic aperture radar (ISAR) imaging method are proposed for millimeter-wave (MMW) imaging of the hidden moving target. In the scheme, a classical threshold method is used to remove the interference signals of the stationary background and extract the triple-reflected echo of the hidden moving target. Then, the image focusing on the moving target is achieved by the range migration algorithm (RMA) with the mirror projection of the wall. Finally, a near-field NLOS experiment system for the hidden rotating target was constructed by TI MMW sensors. The effectiveness of the method is verified by these experiments.