Moving Target Imaging for Synthetic Aperture Radar Via RPCA
Moving Target Imaging for Synthetic Aperture Radar Via RPCA
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DOI：
10.1109/radarconf2147009.2021.9455293
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发表时间：
2021-05
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通讯作者：
Sean Thammakhoune;Bariscan Yonel;Eric Mason;B. Yazıcı;Yonina C. Eldar
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作者：
Sean Thammakhoune;Bariscan Yonel;Eric Mason;B. Yazıcı;Yonina C. Eldar
移动目标的合成孔径雷达 (SAR) 成像是一项具有挑战性的任务，因为已经开发了针对静止场景的标准技术。受视频处理变化检测中鲁棒主成分分析 (RPCA) 成功的启发，我们为图像域中的 SAR 问题建立了秩 1 和稀疏分解框架。我们构造了单通道 SAR 系统以各种假设速度重建图像的相空间反射率矩阵，并表明它是 1 阶矩阵和不相交稀疏矩阵的叠加。与通用 RPCA 相比，这种结构允许额外的约束，从而降低计算复杂性。我们比较了两种算法：近端梯度下降法（PGD）和乘法器交替方向法（ADMM）在运动目标成像问题数值模拟中的性能。
Synthetic aperture radar (SAR) imaging of moving targets is a challenging task, as standard techniques have been developed for stationary scenes. Motivated by success of robust principal component analysis (RPCA) in change detection for video processing, we establish a rank-1 and sparse decomposition framework for the SAR problem in the image domain. We construct the phase-space reflectivity matrix for single-channel SAR systems reconstructing images at various hypothesized velocities and show that it is the superposition of a rank-1 matrix and a disjoint sparse matrix. This structure allows for additional constraints that reduce the computational complexity when compared to generic RPCA. We compare the performances of two algorithms, proximal gradient descent (PGD) and alternating direction method of multipliers (ADMM), on numerical simulations for the moving target imaging problem.