Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization
Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization
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DOI：
10.1109/tci.2021.3111580
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发表时间：
2021-05
影响因子：
5.4
通讯作者：
Sean Thammakhoune;Bariscan Yonel;Eric Mason;B. Yazıcı;Yonina C. Eldar
中科院分区：
文献类型：
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作者：
Sean Thammakhoune;Bariscan Yonel;Eric Mason;B. Yazıcı;Yonina C. Eldar
在本文中，我们提出了一种使用合成孔径雷达进行地面移动目标成像（GMTI）和速度恢复的方法。我们将 GMTI 问题表述为相空间反射率 (PSR) 函数的恢复，该函数表示感兴趣场景中散射体的强度和速度。我们证明，离散化的 PSR 矩阵可以分解为一个秩一的分量，以及一个分别对应于静止和移动散射体的高度稀疏分量。然后，我们通过解决约束优化问题来恢复这两个不同的分量，该问题允许在乘法器框架的近端梯度下降和交替方向方法内使用计算高效的凸求解器。利用 PSR 矩阵的结构特性，我们减轻了与秩约束相关的计算成本高昂的步骤，例如奇异值阈值化。与最先进的 GMTI 方法相比，我们基于优化的方法具有多个优势，包括计算效率、对密集目标环境的适用性以及任意成像配置。我们进行了广泛的模拟，以评估我们的方法对加性噪声和杂波以及移动目标数量不断增加的鲁棒性。我们表明，这两种求解器在密集的移动目标环境和低信杂比环境中都表现良好，而无需额外的杂波抑制技术。
In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly sparse component corresponding to the stationary and moving scatterers, respectively. We then recover the two distinct components by solving a constrained optimization problem that admits computationally efficient convex solvers within the proximal gradient descent and alternating direction method of multipliers frameworks. Using the structural properties of the PSR matrix, we alleviate the computationally expensive steps associated with rank-constraints, such as singular value thresholding. Our optimization-based approach has several advantages over state-of-the-art GMTI methods, including computational efficiency, applicability to dense target environments, and arbitrary imaging configurations. We present extensive simulations to assess the robustness of our approach to both additive noise and clutter, with increasing number of moving targets. We show that both solvers perform well in dense moving target environments, and low-signal-to-clutter ratios without the need for additional clutter suppression techniques.