Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery

Ultra-wideband frequency modulated continuous wave （FMCW） radar has the ability to achieve high-range resolution. Combined with the inverse synthetic aperture technique, high azimuth resolution can be realized under a large rotation angle. However, the range-azimuth coupling problem seriously restricts the inverse synthetic aperture radar （ISAR） imaging performance. Based on the turntable model, traditional match-filter-based, range Doppler algorithms （RDAs） and the back projection algorithm （BPA） are investigated. To eliminate the sidelobe effects of traditional algorithms, compressed sensing （CS） is preferred. Considering the block structure of a signal at high resolution, a block-sparsity adaptive matching pursuit algorithm （BSAMP） is proposed. By matching pursuit and backtracking, a signal with unknown sparsity can be recovered accurately by updating the support set iteratively. Finally, several experiments are conducted. In comparison with other algorithms, the results from processing the simulation data, some simple targets, and a complex target indicate the effectiveness and superiority of the proposed algorithm.

超宽带调频连续波雷达可凭借大带宽获得极高的距离分辨率。同时,结合逆合成孔径技术,在大转角条件下进一步得到二维高分辨目标图像。然而,在逆合成孔径雷达中的距离—方位耦合问题严重制约了成像性能。本文基于转台模型的假设,研究传统的匹配滤波类算法,如距离多普勒算法和后向投影算法。为消除传统算法中的高旁瓣效应,进一步探索压缩感知类算法。考虑到高分辨条件下信号的块稀疏特性,本文重点研究了基于块稀疏的自适应匹配追踪大转角ISAR(inverse synthetic aperture radar)成像算法。通过信号对字典的匹配追踪与回溯更新,将雷达回波在未知稀疏度下精确重构,最终得到目标ISAR图像。计算机仿真、简单目标和复杂目标实验结果证明了该算法相比其他算法的有效性和优越性。