多重测量矢量模型下的稀疏步进频率SAR成像算法

基于压缩感知(Compressed Sensing, CS)的合成孔径雷达(SAR)成像算法可以用低于Nyquist采样率的采样数据完成稀疏目标高分辨成像。然而已有的算法在重构1维距离像时采用的大都是单重测量矢量(Single Measurement Vectors, SMV)模型，存在着重构耗时长、受噪声干扰大的缺点。该文从压缩感知的多重测量矢量(Multiple Measurement Vectors, MMV)模型出发，利用多重测量矢量恢复具有相同稀疏结构的联合稀疏目标信号源，从理论与实验角度分析了基于MMV模型的SAR 1维距离像成像性能，提出了一种距离向基于MMV模型，方位向基于SMV模型的2维SAR成像算法。该算法从耗时上、重构精度上均优于SMV模型下的CS成像算法。通过对仿真数据和地基雷达实测数据的处理，验证了算法的有效性。

An Imaging Algorithm of Sparse Stepped Frequency SAR Based on Multiple Measurement Vectors Model

The SAR imaging algorithm based on Compressed Sensing (CS), could complete the high-resolution imaging of sparse target with the sampling data below the Nyquist sampling rate. However, the Single Measurement Vectors (SMV) model used for range profile reconstruction in existing algorithms, is time-consuming and noise-affected. Based on the Multiple Measurement Vectors (MMV) model, this paper proposes to recovery the joint sparse target signal source of the same sparse structure by MMV. The range profile imaging performance is analyzed theoretically and experimentally. Then, a 2-D SAR imaging algorithm, in which the range imaging is realized based on MMV model and azimuth imaging is realized based on SMV model, is proposed. This algorithm is superior to the SMV-based CS algorithm both on time-consuming and reconstruction precision. The processing of simulation data and radar measured data verifies the effectiveness of this algorithm.