基于稀疏重构的海杂波抑制和目标提取算法  

Sea clutter suppression and target extraction algorithm based on sparse reconstruction

作　　者：李文静 李卓林[1] 袁振涛 LI Wenjing;LI Zhuolin;YUAN Zhentao(Beijing Institute of Radio Measurement,Beijing 100854,China;Air Force Academy,Beijing 100085,China)

机构地区：[1]北京无线电测量研究所,北京100854 [2]空军研究院,北京100085

出　　处：《系统工程与电子技术》2022年第3期777-785,共9页Systems Engineering and Electronics

摘　　要：在杂波较强的环境下,雷达目标回波往往淹没在杂波中难以被检测,尤其在海杂波背景下,目标的多普勒频率有可能会落在杂波频率范围中,此时传统的杂波抑制方法就产生了一定的局限性。针对此问题,依据杂波、目标信号的稀疏特性和二者在多普勒频率分布特性上的不同,设计了对应的时频域过完备字典;再通过形态成分分析算法求出目标和杂波分量的稀疏系数向量;将对应字典和稀疏系数向量相乘,恢复出目标和杂波分量,同时实现了杂波抑制和目标信息提取。最后,通过实测数据验证了该算法的有效性。In a strong clutter environment, radar target echoes are often submerged in clutter and difficult to detect. Especially under the background of sea clutter, the Doppler frequency of target may fall in clutter frequency range. At this time, traditional clutter suppression method has limitations. In response to this problem, corresponding time-frequency domain over-complete dictionaries are designed, which are based on the sparse characteristics and the differences in Doppler frequency range of target and clutter. Then use morphological component analysis algorithm to find the sparse coefficients of target and clutter components. The target and clutter components are recovered by multiplying the corresponding dictionary and sparse coefficients, achieving clutter suppression and target information extraction. Finally, the effectiveness of the algorithm is verified by measured data.

关 键 词：杂波抑制 稀疏重构 形态成分分析 目标提取 

分 类 号：TN957[电子电信—信号与信息处理]