基于联合稀疏功率谱恢复的机载雷达稳健S TA P算法研究

摘要

Space-time adaptive processing (STAP)needs to estimate clutter covariance matrix by training sample da-ta.However,this estimation is always corrupted by outliers,which even lead to target self-nulling phenomenon.Hence,a no-vel robust STAP algorithm based on joint sparse recovery of clutter spectrum is proposed,which can eliminate the influence of outlier.This algorithm is applied in side-looking airborne radar.When the sparse recovery is high resolution,the algorithm exploits the characteristic that distribution and correlation between clutter and outlier are different among multiple snapshots. The norm is employed to select the most suitable sparse recovery coefficients to estimate the clutter spectrum,so outlier can be eliminated effectively.Monte Carlo experiments prove that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms in non-homogeneous clutter environments.%机载雷达信号的空时自适应处理（STAP，Space-Time Adaptive Processing）需要利用样本数据来估计杂波协方差矩阵．非均匀杂波环境中的离群点会使协方差矩阵的估计出现偏差，从而导致信号相消现象．针对此问题，本文提出一种基于联合稀疏功率谱恢复的STAP算法（JSR-STAP）处理非均匀杂波，以克服离群点对正侧视模式机载雷达的STAP性能的影响．JSR-STAP算法在高分辨稀疏恢复的条件下，利用多快拍间杂波和离群点的分布规律和相关性不同，通过范数选择合适的稀疏恢复系数来实现离群点的抑制．Monte Carlo实验证明JSR-STAP算法的稳健性和动目标检测性能均优于传统的STAP算法．