基于压缩感知理论的雷达成像技术与应用研究进展

Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory

压缩感知理论基于信号稀疏性,将对信号采样转换为对信息自由度的采样,可大大降低采样率。而将压缩感知理论应用于雷达成像时有望在以下几个方面得到改善:增强成像性能,简化雷达硬件设计,缩短数据获取时间,减少数据量和传输量等。该文从压缩感知的稀疏性,压缩采样,无模糊重建3个关键步骤与成像雷达有机结合的角度,对近年来基于压缩感知理论的雷达成像技术研究现状进行系统综述,重点论述场景稀疏性与成像关系,压缩采样方法(包括硬件)设计,场景图像快速高精度重建以及成像系统体制应用等方面,最后探讨了压缩感知理论应用尚需解决的问题和进一步发展方向。

Compressive Sensing（CS） theory, based on the sparsity of interested signal, samples degree-of-freedom of signal. CS is expected to improve the performance of imaging radar in the following aspects： improving the quality of imaging, simplifying the designing of radar hardware, shortening the imaging time and compressing data. This paper first combines the analysis of radar imaging with the three aspects of CS, namely the sparsity of interested signal, the compressive sampling and optimization method. Thereafter a particular and comprehensive review of CS theory in imaging radar is summarized, mainly including the relationship between sparsity of the scene and imaging, compressive sampling methods, fast and accurate reconstruction of the scene and the applications to different imaging radar systems. Finally, the unresolved problems in current research and further study directions are pointed out.