基于贪婪-快速阈值迭代的SAR地面动目标稀疏表征算法

合成孔径雷达地面动目标成像(Synthetic Aperture Radar Ground Moving Target Imaging, SAR-GMTIm)技术通过在静止场景的SAR图像中检测运动目标响应,实现针对运动目标的重聚焦成像。通常情况下,地面运动目标回波响应相对于静止场景的回波(即杂波)具有较强的稀疏性,增强SAR-GMTIm成像结果的稀疏特征有利于目标分类和识别。现有的一阶算法如阈值迭代算法(Iterative Shrinkage-thresholding Algorithm,ISTA)及其改进方法,快速阈值迭代算法(Fast Iterative Shrinkage-thresholding Algorithm,FISTA)都可用于SAR-GMTIm稀疏特征增强,但都存在运算效率偏低,收敛速度较慢的问题。针对以上问题,本文提出了一种贪婪-快速阈值迭代算法(Greedy Fast Iterative Shrinkage-thresholding Algorithm,Greedy FISTA)用于SAR-GMTIm稀疏特征恢复。该算法基于重启动框架对FISTA进行改进,缩短了算法重启间隔和振荡周期,拥有比FISTA更快的收敛速度。本文利用Greedy FISTA针对SAR-GMTIm的仿真复数据以及美国空军实验室的Gotcha实测雷达数据进行成像实验,并对比Greedy FISTA和FISTA、ISTA在SAR动目标成像中达到同等精度所需的迭代次数,再结合相变热力图分析法对比三种算法的恢复性能。实验结果表明Greedy FISTA应用于SAR-GMTIm系统具有良好的成像效果, 且在收敛速度和稀疏信号恢复方面相较传统阈值迭代算法及快速阈值迭代算法有明显优势。 

Sparse representation for SAR ground moving target imaging based on Greedy FISTA

Synthetic Aperture Radar Ground Moving Target Imaging (SAR-GMTIm) is capable of detecting moving target responses in the stationary clutter and forming focused images of the target. There is a widely accepted fact that the moving target response is inherently sparse with respect to the rich clutter. To achieve the recovery of the sparse response of the moving target, algorithms of the first-order are popular, where ISTA and FISTA are typical candidates that can be applied to image for sparse SAR moving target. However, there are some problems such as low computation efficiency and slow convergence. To address the problems, a Greedy Fast Iterative Shrinkage-thresholding Algorithm (Greedy FISTA) is proposed for the representation of the sparse features of SAR-GMTIm. This algorithm improves the FISTA based on the restart framework, and shortens the restart interval and oscillation period of the algorithm, so that a faster convergence rate can be ensured compared with the conventional FISTA. In this paper, both simulated complex SAR data and raw Gotcha data from US Air Force Laboratory are applied to examine the performance of the proposed Greedy FISTA algorithm. Comparisons with the conventional ISTA and FISTA in terms of the required number iteration are performed, and the performance of the sparse recovery are examined by using the phase transition diagram (PTD) analysis. Experimental results show that Greedy FISTA has good imaging performance when applied to the SAR-GMTIm data, and has obvious advantages over other threshold iterative algorithms in terms of convergence speed and sparse recovery accuracy.