知识辅助的机载MIMO雷达降秩STAP算法

摘要

Space-time adaptive processing (STAP) for airborne MIMO radar is studied based on multistage Winner filter with generalized side-lobe canceller structure, and a new reduced-rank STAP algorithm using a priori knowledge constraint is proposed for MIMO radar. Through the use of jamming direction knowledge and clutter subspace knowledge estimated by prolate spheroidal wave functions, the algorithm can significantly reduce computation and sample demand of airborne MIMO radar STAP, and keep the performance of clutter suppression simultaneously. Also, the knowledge-aided effect on the convergence performance is considered when the knowledge is mismatched. Simulation results show that when there is a certain error of knowledge, the algorithm can still improve the convergence of STAP algorithm effectively.%研究机载平台下的MIMO雷达空时自适应处理技术(STAP),基于广义旁瓣相消结构的多级维纳滤波器,提出了一种利用先验知识约束的MIMO雷达降秩STAP算法.通过利用干扰方向知识以及基于扁长椭球波函数估计的杂波子空间知识,在保证杂波抑制性能的基础上,大大降低了机载MIMO雷达STAP算法的运算量和样本需求量.同时,考虑了知识不匹配情况下,知识辅助对算法收敛性能的影响.仿真结果表明,当知识存在一定误差时,该算法仍能有效地改善STAP算法的收敛性能.