多级嵌套维纳滤波自适应降维STAP抗干扰方法研究

Research on Adaptive Reduced-Dimension STAP Anti-Interference Based on Multistage Nested Wiener Filter

在分析多级嵌套维纳滤波(MSNWF)给出的干扰子空间和噪声子空间信息的基础上,提出了多级嵌套维纳滤波自适应降维空时自适应处理(STAP)抗干扰算法。该方法分析了MSNWF前向迭代过程中相邻级期望信号的协方差随迭代次数变化关系,结合干扰子空间和噪声子空间信息自适应地确定迭代次数,在不增加额外计算量的条件下实现了降维维数的自适应选择。仿真结果分析表明,该方法在保证抗干扰性能不受影响的前提下,进一步减小了STAP算法的计算量,提高了算法的实时性。

An adaptive reduced-dimension space-time adaptive process （ STAP） anti-interference method is proposed on the basis of analysis of the information of interference subspace and noise subspace provided by MSNWF. This method is used to analyze the varying trend of the cross-correlations between the adjacent desired signals in the forward interaction of MSNWF. Then optimal iterations are obtained by taking the information of interference subspace and noise subspace into account meanwhile. Thus optimal dimension can be adaptively selected without increasing the computational effort. Analysis and results of the experiment show that the proposed method further reduces the computational effort of STAP and enhances its real-time operation with the anti-interference performance unaffected.