Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel and efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-calculated as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. Furthermore, the introduction of an adaptive threshold for each azimuth-range pixel permits the threshold shrinkage to be not only layer-varied but also element-wise. Additionally, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.

Synthetic aperture radar tomography (TomoSAR) retrieves 3-D information from multiple synthetic aperture radar (SAR) images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on $\ell _{1}$ norm regularization and other grid-based CS (GBCS) methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the “off-grid” effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This article presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vector (MMV) model is constructed to enhance noise immunity without compromising azimuth–range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding (AST) algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. The validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

Automotive millimeter-wave radar sensor stands out for its weather-independent capability, serving as an indispensable tool in advanced driver assistance systems. Accurate and efficient direction-of-arrival (DOA) estimation is crucial for automotive tasks such as object detection, tracking, and collision avoidance. While recent popular single snapshot DOA estimation algorithms based on compressed sensing offer super-resolution capabilities, they are highly sensitive to mismatches between assumed and actual sparsity bases, known as the off-grid effect. To address this issue, atomic norm minimization (ANM), a promising gridless sparse recovery algorithm under the Toeplitz model, has gained prominence in DOA estimation for uniform and sparse linear arrays. However, cost and hardware limitations of automotive radar sensors often result in non-uniform array structures, obstructing the application of ANM-based gridless DOA estimation methods. This paper presents a novel gridless DOA estimation method for non-uniform linear arrays (NLA) based on ANM with efficiency. It achieves this by transforming NLA manifolds into Vandermonde vectors corresponding to virtual uniformly spaced arrays, and employing accelerated proximal gradient techniques while leveraging the Vandermonde structure and low-rank property. This approach ensures the applicability of ANM-based algorithms to NLA without sacrificing efficiency. Simulation and measurement experiments on automotive radar sensors demonstrate its superiority.

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination.

Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy.

Tomographic synthetic aperture radar (TomoSAR) has garnered significant attention recently due to its 3-D imaging capabilities. However, interchannel phase errors present in practical applications severely degrade the quality of 3-D reconstruction results. The current state-of-the-art methods for interchannel phase error estimation and compensation fall into two categories: calibrator-based methods and data-driven methods relying on synthetic aperture radar echo measurements. These methods face challenges, such as unavailability of the calibrator, reliance on single constraint criteria, and heavy computational burden. In this article, we propose an innovative and efficient method for estimating interchannel phase errors in TomoSAR imaging. We consider the phase errors as parameters to be estimated and optimize the estimation based on a joint constraint criterion of minimum mean square error and minimum entropy. In addition, our approach introduces a joint iterative solution framework, which incorporates a modified accelerated iterative shrinkage-threshold sparse recovery algorithm and an adaptive momentum optimizer for interchannel phase error estimation in a gradient descent manner. Compared to conventional phase error calibration methods in a two-step iterative framework, our proposed method considers image features and parameter coupling relationships, thus achieving higher precision estimation while saving computational costs. Finally, we demonstrate the effectiveness and applicability of the proposed method based on simulation experiments and real data experiments of the SARMV3D dataset.