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.