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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。
In this paper, MIMO radar space-time adaptive processing (STAP) algorithm based on airborne platform and multistage Wiener filter based on generalized side lobe cancellation structure are proposed. A rank reduction STAP algorithm based on prior knowledge constraint is proposed. By using the knowledge of interference direction and the knowledge of clutter subspace based on the prolate spheroidal wave function, the computational complexity and sample requirements of the STAP algorithm for onboard MIMO radar are greatly reduced while ensuring clutter suppression performance. At the same time, considering the mismatch of knowledge, the influence of knowledge aiding on the convergence performance of the algorithm. The simulation results show that the proposed algorithm can effectively improve the convergence performance of STAP algorithm when there is some error in knowledge.