稳健的基于参数化协方差矩阵估计的空时自适应处理方法

参数化协方差矩阵估计（Parametric Covariance Matrix Estimation，PCE）方法利用雷达系统参数估计杂波协方差矩阵（Clutter Covariance Matrix，CCM），显著提升非均匀环境下空时自适应处理（Space-Time Adaptive Processing，STAP）的性能；但是在系统参数和杂波分布存在误差情况下，性能下降严重.本文提出一种稳健的基于PCE方法的STAP杂波抑制方法.首先利用稀疏恢复方法与Radon变换估计杂波分布，然后提出一种归一化广义内积统计量修正杂波的分布，最后利用PCE方法估计CCM并进行STAP杂波抑制.通过分析舰载高频地波雷达仿真和实测数据处理结果表明：所提方法的稳健性大幅提升，相比稀疏恢复STAP方法和前后向空时平滑STAP方法滤波器凹口更加准确且更深，在有效抑制杂波的同时更利于慢速目标的检测.

Robust Parametric Covariance Matrix Estimation Based STAP Method

The parametric covariance matrix estimation (PCE) method uses the system parameters to estimate the clutter covariance matrix (CCM).It can greatly improve the performance of space-time adaptive processing (STAP) in nonhomogeneous environment.However,the performance of PCE method is seriously degraded when the system parameter information or clutter distribution is in error.This paper presents a robust parametric covariance matrix estimation based STAP method.First the clutter distribution is estimated by the sparse recovery (SR) and Radon transform.Then a normalized generalized inner product statistic (N-GIP) is proposed to modify the clutter distribution parameters.Finally,the PCE method is utilized to estimate the CCM and the STAP is used to suppress clutter.The simulation experiments and measured data processing results show that the robustness of the proposed method is greatly improved.Compared with the sparse recovery STAP (SR STAP) and forward/backward smoothing STAP (F/B STAP),the filter notches are more accurate and deeper.This benefits the detection of slow targets.