Concealed moving target imaging is an important task in the fields of autonomous driving, urban combat, city anti-terrorism, etc. However, existing radar imaging techniques mainly focus on line-of-sight (LOS) targets and struggle to sense non-line-of-sight (NLOS) moving targets. In this paper, a joint NLOS model and inverse synthetic aperture radar (ISAR) algorithm, dubbed corner motion target imaging (CMTI), is proposed for moving target imaging via millimeter-wave (MMW) radar. In this scheme, initially, to separate hidden motion target echoes and background clutter, we utilize a low-frequency filtering method to suppress the clutter and an NLOS echo extraction method based on OTSU to extract the target echoes. Subsequently, to solve the problem of focusing and imaging difficulties, a novel NLOS imaging method based on the polar format algorithm (PFA) and the prominent point processing (PPP) autofocus algorithm is exploited. The method consists of a cross-correlation method and a minimum variance algorithm for envelope alignment and phase adjustment, the minimization entropy autofocusing (MEA) method and PPP for residual phase error and target rotational speed estimation, and PFA for 2- D high-resolution imaging. Eventually, to obtain the real target image, we present a reconstruction algorithm based on the mirror symmetry criterion. Near-field experiments are set up to verify CMTI. The experimental results demonstrate that CMTI can provide high-resolution images of the NLOS moving targets

Inverse Synthetic Aperture Radar (ISAR) is a promising technique for air target imaging and recognition. However, the traditional monostatic ISAR only can provide partial features of the observed target, which is a challenge for high-accuracy recognition. In this paper, to improve the recognition accuracy of air targets, we propose a novel recognition network based on multi-view ISAR imaging and fusion, called Multi-View Fusion Recognition network (MVFRnet). The main structure of MVFRnet consists of two components, the image fusion module and the target recognition module. The fusion module is used for multi-view ISAR data and image preprocessing and mainly performs imaging spatial match, image registration, and weighted fusion. The recognition network consists of the Skip Connect Unit and the Gated Channel Transformation (GCT) attention module, where the Skip Connect Unit ensures the extraction of global depth features of the image and the attention module enhances the perception of shallow contour features of the image. In addition, MVFRnet has a strong perception of image details and suppresses the effect of noise. Finally, simulated and real data are used to verify the effectiveness of the proposed scheme. Multi-view ISAR echoes of six types of aircraft are produced by electromagnetic simulation software. In addition, we also build a millimeter wave ground-based bistatic ISAR experiment system and collect multi-view data from an aircraft model. The simulation and experiment results demonstrate that the proposed scheme can obtain a higher recognition accuracy compared to other state-of-the-art methods. The recognition accuracy can be improved by approximately 30% compared with traditional monostatic recognition.

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.

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 paper, a novel algorithm dubbed NLOS detail recovery via alternating direction method of multipliers (NDR-ADMM) 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, ℓ 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.

Synthetic aperture radar tomography (TomoSAR) can acquire high resolution in height direction by forming a large synthetic aperture along the tomographic direction. Compressed sensing (CS) is widely utilized in TomoSAR imaging to reduce the costs of data sensing. Nevertheless, traditional CS-based algorithms are limited to computational complexity and the non-trivial parameters' tunning. To address such problems, an efficient unfolded deep shrinkage-thresholding network (UDST-net) is proposed for TomoSAR imaging in this paper. The proposed method adopts convolutional neural network module to learn a generalized nonlinear sparse transformation operator, showing great benefits in exploring the optimal prior. Besides, the hyper-parameters of the optimization framework are learned by end-to-end learning mechanism instead of manual-defined which obviously improves the efficiency of imaging process. Inspired by residual network (ResNet), the residual learning is introduced to reconstruction blocks of the proposed imaging network, improving the robustness of the network. In addition, the training dataset is constructed from point cloud data based on TomoSAR imaging principles, enhancing the network's ability to extract structural information. Finally, extensive simulation and measured experimental results show the effectiveness of the proposed method, obtaining high-quality imaging results while maintaining high computational efficiency.

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 sparse reconstruction algorithm based on ADMM, named ATS-ADMM, is proposed. Within our framework, a Detail Enhancement Operator (DEO) is designed and combined with the l₁-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 multi-constrained 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 ATSADMM effectively focuses images, is suitable for diverse imaging scenarios, and is the fastest among ADMM-based algorithms.