基于LRMO及MCA的机载雷达风电场杂波抑制方法

风力发电迅速发展，风电场杂波使机载雷达产生大量虚假目标，导致机载雷达出现检测概率下降、虚警概率上升等问题。因此，研究机载雷达风电场杂波抑制方法对于提升机载雷达工作性能具有十分重要的意义。考虑到机载雷达风电场杂波先验信息无法实时获取、难以估计且机载雷达回波频谱更加复杂等特殊问题，本文基于低秩矩阵优化（LRMO）算法根据风电场杂波与目标微动特征随时间的不同变化特性，实现目标与风电场杂波处于不同距离单元的风电场杂波抑制。目标与风电场杂波处于同一距离单元时，考虑到LRMO算法存在的局限性，依据风电场杂波与目标的不同稀疏特性，利用形态成分分析（MCA）算法进行补充抑制风电场杂波。实验结果验证了所提方法的有效性。 

Clutter Suppression of Wind Farm for Airborne Radar Based on LRMO and MCA

The rapid development of wind power generation has caused the airborne radar to produce a large number of false targets due to the clutter generated by wind farms， leading to problems such as decreased detection probability， increased false alarm rate. Research on wind farm clutter mitigation technology for airborne radar is vital for the performance improvement of airborne radar. Considering the special problems of the wind farm clutter mitigation for airborne radar， such as the less prior information， the limited samples， and the more complicate spectrum of the echoes， the clutter is suppressed according to the different micro-motion feature variance of the wind turbine clutter and target with time based on low-rank matrix optimization （LRMO） algorithm when the clutter and the target are in different distance units. However， when the wind turbine clutter and the target are in the same distance unit， the morphological component analysis （MCA） algorithm is used to mitigate the clutter in which the different sparse characteristics of the target and the clutter is considered. The experimental results verify the effectiveness of the proposed method.