基于压缩感知的条带随机噪声雷达稀疏成像方法

摘要

The compressive sensing (CS) theory and the strip-map random noise radar are connected. A new sparse imaging method with strip-map random noise radar based on CS is proposed on the basis of supposing of sparse scene targets. In the method, the sparse transform matrix is designed as the different delay matrixes of random noises, and the measurement matrix is designed as the product of random noise and partial unit matrix. The number of the sampling data of returned signals is reduced apparently, and the high quality scene image can be obtained by using the method. Simulation results prove the effectiveness and robustness of the proposed method.%将压缩感知理论与条带随机噪声雷达相结合,在假设场景目标稀疏的前提下,通过构造随机噪声的不同时延矩阵为稀疏变换矩阵以及通过构造随机噪声与部分单位阵的乘积为观测矩阵,提出了一种基于压缩感知的条带随机噪声雷达稀疏成像方法.该方法能在大幅减少回波信号采样数据量的前提下,准确重建出原始场景目标高分辨像.仿真结果证明了该方法的有效性与鲁棒性.