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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。
Compressed sensing theory can solve the large-bandwidth, multi-channel radar system storage and transmission of large amounts of data. In this paper, compressed sensing theory is applied to radar high-resolution imaging. The radar imaging algorithm based on regular matching pursuit algorithm (ROMP) is studied and compared with the algorithms based on smooth 0-norm (SL0) and 1-norm optimization L1) radar imaging algorithm is contrasted. Through numerical simulation experiments, the effectiveness of these three imaging algorithms is verified. Simulation results show that ROMP-based compress-aware radar imaging algorithm is superior to compress-aware radar imaging algorithms based on SL0 and L1 norm in terms of computational speed.