稀疏恢复频率步进雷达的扩展距离像
Extended Range Profiling in Stepped-Frequency Radar with Sparse Recovery
摘要：
压缩感知（CS）理论是一种新兴的理论，它能够从数量不足的线性投影中恢复稀疏信号。基于压缩感知理论，提出了一种频率步进雷达在脉冲丢失情况下的高分辨距离像提取算法。新算法在多个粗距离单元上恢复目标距离像，提供了宽距离像能力。MATLAB仿真结果验证了该方法的有效性。此外，我们使用实际SF雷达收集的数据来生成扩展的目标高分辨率距离（HRR）轮廓。与基于“拉伸”的最小二乘法的结果进行了比较，以证明其适用性。
英文摘要：
The newly emerging theory of compressed sensing (CS) enables restoring a sparse signal from inadequate number of linear projections. Based on compressed sensing theory, a new algorithm of high-resolution range profiling for stepped-frequency (SF) radar suffering from missing pulses is proposed. The new algorithm recovers target range profile over multiple coarse-range-bins, providing a wide range profiling capability. MATLAB simulation results are presented to verify the proposed method. Furthermore, we use collected data from real SF radar to generate extended target high-resolution range (HRR) profile. Results are compared with `stretch' based least square method to prove its applicability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
