基于ROMP 的压缩感知算法在雷达成像中的应用

压缩感知理论能够解决大带宽、多通道雷达系统的大数据量存储和传输问题。本文将压缩感知理论应用到雷 达高分辨率成像中，研究了基于正则匹配追踪算法（ROMP）的雷达成像算法，并把它和基于平滑0-范数（SL0）优化 和1-范数优化(L1)的雷达成像算法做了对比。通过对数值仿真实验，验证了这三种成像算法的有效性。仿真结果表 明基于ROMP 的压缩感知雷达成像算法在计算速度方面优于基于SL0 和L1 范数的压缩感知雷达成像算法。

Compressive Radar Imaging Algorithm Based on Regularized Orthogonal Matching Pursuit Methods

Compressive sensing theory is able to solve the problem of data store and transportation introduced by a large bandwidth and an increasing number of channels. Compressive sensing theory is applied to high resolution radar imaging. An compressive radar imaging method based on Regularized Orthogonal Matching Pursuit (ROMP) is proposed and compares with the compressive radar imaging method based on Fast Smoothed L0 (SL0) and L1 norm. Through numerical simulation, the focusing performance of the three algorithms is good and the compressive radar imaging method based on ROMP is much faster than the algorithms based on SL0 and L1 norm.