吕明久, 李少东, 杨军, 马晓岩. 频率步进信号距离-方位联合超分辨成像方法[J]. 电波科学学报, 2016, 31(3): 537-545. doi: 10.13443/j.cjors.2015083103
      引用本文: 吕明久, 李少东, 杨军, 马晓岩. 频率步进信号距离-方位联合超分辨成像方法[J]. 电波科学学报, 2016, 31(3): 537-545. doi: 10.13443/j.cjors.2015083103
      LÜ Mingjiu, LI Shaodong, YANG Jun, MA Xiaoyan. Range-azimuth joint ISAR super-resolution imaging method based on frequency-stepped signal[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(3): 537-545. doi: 10.13443/j.cjors.2015083103
      Citation: LÜ Mingjiu, LI Shaodong, YANG Jun, MA Xiaoyan. Range-azimuth joint ISAR super-resolution imaging method based on frequency-stepped signal[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2016, 31(3): 537-545. doi: 10.13443/j.cjors.2015083103

      频率步进信号距离-方位联合超分辨成像方法

      Range-azimuth joint ISAR super-resolution imaging method based on frequency-stepped signal

      • 摘要: 对距离向、方位向二维稀疏的频率步进信号模型, 基于压缩感知理论, 研究了一种适用于稀疏频率步进回波的距离-方位联合逆合成孔径雷达(Inverse Synthetic Aperture Radar, ISAR)超分辨成像方法.首先对稀疏频率步进回波进行建模, 在此基础上构建了距离向和方位向二维稀疏时的联合稀疏基, 最后利用二维平滑0-范数法在矩阵域直接进行处理, 得到最终的ISAR超分辨成像结果.并对算法复杂度、超分辨性能进行了分析, 得出了相应的结论.理论分析和仿真结果表明所提方法在不同稀疏方式、不同稀疏条件下具有更好成像质量、更快处理速度的优势.

         

        Abstract: For the sparse frequency-stepped (SFS) radar signal which is sparse both in range and azimuth, a novel range-azimuth joint ISAR Imaging method is proposed based on compressive sensing (CS) theory. Firstly, the echo model of the SFSS is established. Secondly, the range-azimuth joint sparse base is built, which is sparse both in the range and azimuth dimension. Finally, the super-resolution ISAR Imaging can be realized via the 2D-Smoothed L0 algorithm which can dispose this echo model in matrix domain directly. Some performances of the proposed algorithm such as complexity and the super resolution capability are also analyzed and some corresponding results are given. At last, theoretical analysis and simulation experiments verify that the proposed algorithm have a better imaging performance and a faster processing speed at different sparse conditions and sparse patterns.

         

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