Low complexity single dataset STAP for nonstationary clutter suppression in HF mixed-mode surface wave radar

摘要

The nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational complexity as well as the inaccurate estimation of the clutter covariance matrix restricts the practical application of these methods. In this letter, we propose a novel reduce-rank-based single dataset STAP to suppress the nonhomogeneous clutter in practical HF radar system. A fast implementation of subspace tracking algorithm is introduced to estimate the clutter subspace as well as reduce the computational cost via pulse iteration method. The effectiveness of the proposed method is verified by both simulated and experimental data. The results show it outperforms traditional single dataset STAP methods and the nonstationary clutter can be greatly suppressed.