基于压缩感知的雷达目标距离超分辨成像算法

Range Super-resolution Imaging for Radar Targets Based on Compressive Sensing

针对传统雷达匹配滤波处理的距离分辨严格受限于信号带宽的问题,提出了基于压缩感知的雷达目标距离超分辨算法并对其进行了性能分析。通过数学推导给出了距离超分辨算法的理论模型和求解算法,通过仿真实例分析了不同信噪比条件下算法的性能边界。基于某雷达实录的目标回波数据验证了算法的性能,仿真和实录数据表明,基于压缩感知的超分辨算法在信噪比≥20 dB条件下,可精确获取6倍以上的距离高分辨估计,估计误差小于-10 dB。

The radar range resolution of conventional matched filter is limited strictly by the signal bandwidth. In order to obtain super-resolution within limited signal bandwidth, a range super-resolution imaging for radar targets using compressive sensing is proposed. The theoretical model and algorithm for range super-resolution are presented. The performance analysis on different conditions of SNR(Signal-to-Noise Ratio) is researched by simulations. Moreover, experimental results are presented. The simulation and experimental results indicate that the range super-resolution can be improved more than six-times than traditional pulse compress processing with estimation error less than-10 dB, when target′s SNR is larger than 20 dB.