This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent in the constructed Hankel matrix of the mixed-precision array, to recover azimuth angles from quantized measurements. Our proposed approach addresses the challenge of mixed-quantized Hankel matrix completion, allowing for accurate estimation of the azimuth angles of interest. To evaluate the recovery performance of the proposed scheme, we establish a quasi-isometric embedding with a high probability for mixed-precision quantization. The effectiveness of our proposed scheme is demonstrated through numerical results, highlighting successful reconstruction.

Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren’t accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has a high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.

Tomographic Synthetic aperture radar (TomoSAR) imaging algorithms for urban areas based on Compressed Sensing (CS) often have high time complexity due to many times iterations. Moreover, the phase error (PE) that exists will defocus TomoSAR imaging results. To reduce PE in the TomoSAR process, researchers use methods such as PS-InSAR, phase gradient autofocusing (PGA), etc. However, these methods are computationally expensive, which hinders the application of fast high-resolution TomoSAR imaging. In this paper, we merge the PE compensation into FISTA framework and proposed a novel deep learning approach for TomoSAR imaging. The network is based on Ada-LFISTA architecture, dubbed as AdaTomo-Net. Experiment results show that AdaTomo-Net has higher imaging accuracy and considerable computational efficiency compared with typical CS algorithms and learning-based algorithms such as LISTA in the presence of PE.

Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on deep learning can effectively reduce computational costs. The idea of existing researches is to reconstruct the elevation for each range-azimuth cell in one-dimensional using a deep-unfolding network. However, since these methods are commonly sensitive to signal sparsity level, it usually leads to some drawbacks like continuous surface fractures, too many outliers, et al. To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed. By adding a U-Net-like structure, AETomo-Net performs reconstruction by each azimuth-elevation slice and adds 2D features extraction and fusion capabilities to the original deep unrolling network. In this way, each azimuth-elevation slice can be reconstructed with richer features and the quality of the imaging results will be improved. Experiments show that the proposed method can effectively solve the above defects while ensuring imaging accuracy and computation speed compared with the traditional ISTA-based method and CV-LISTA.

Phase filtering is a key step in the interferometric synthetic aperture radar (InSAR). Compared with the traditional method, the deep learning-based phase filtering method is superior in both accuracy and speed. However, traditional deep learning overly relies on huge data volume and is not interpretable and unstable for the purely data-driven and black-box properties. Therefore, a sparse model-based network for interferometric phase denoising (PD-SMNet) is proposed in this paper which joins conventional ISTA algorithm with the theory basis into a network structure. In contrast with the conventional network, the PD-SMNet is interpretable and more stable, and has good performance on small training samples. The experimental results on simulated and measured data show the proposed method significantly outperforms the previous three widely-used methods in both precision and speed. In addition, the proposed method has higher accuracy on small training sets than conventional deep learning.

In Interferometric Synthetic Aperture Radar (InSAR) data processing, phase filtering has a great impact on the accuracy of the resulting DEM and is therefore an inevitable step. Recently, convolutional neural networks are applied to InSAR phase filtering and show excellent performance, however, most of these deep learning-based methods do not make full use of the self-similarity of phase map, that is, pixels in different or far regions have a close relationship in values or distribution. In this paper, we propose a phase filtering method based on transformer network, which has the advantage of capturing the long-range dependency or exploiting the global features of the phase map, moreover, the deformable convolution is introduced to our method to further extract the local phase features. Experiments validate the efficiency and effectiveness of the proposed method to InSAR phase filtering.

In recent years, 3-D synthetic aperture radar (SAR) imaging has proved its great potential in monitoring, security inspection, and radar cross section (RCS) measurement. However, 3-D SAR images based on matched filter (MF) methods have high sidelobes and are susceptible to background noise. Therefore, in this article, we propose a novel 3-D sparse SAR imaging method to improve the image quality, which combines the plug-and-play framework and the improved alternating direction method of multiplier (ADMM). First, the plug-and-play framework allows one to use state-of-the-art denoisers instead of proximal operators to improve the image quality. Second, we linearize the subproblem of ADMM involving forward imaging model. Compared with the traditional ADMM method, the improved ADMM, namely, linear ADMM (LADMM), avoids the inversion of high-dimensional matrix and is more suitable for solving high-dimensional imaging problems. Simulation and real data experiments show that the proposed method can effectively improve the image quality. Numerical analysis and 3-D visualization results are presented, which prove the impressive performance of plug-and-play LADMM.