Reduced-Dimension Space-Time Adaptive Processing for Sea Clutter Suppression in HFSSWR

The non-homogeneous clutter environment is the biggest challenge for sea clutter suppression in the background of high frequency hybrid sky-surface wave radar (HFSSWR). Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the practical radar system parameters and the training sample starved scenario degrade the performance of STAP. The reduced-dimension STAP algorithm and the valid training sample selection strategy are required in the background of HFSSWR system. In this paper, a novel reduced-dimension STAP method based on Euclidean distance to select the training samples whose clutter covariance matrices are similar to the CUT is presented. Firstly, the joint domain localized (JDL) algorithm is introduced to the application of clutter suppression in HFSSWR. Then, an improved joint domain localized algorithm, which we call Euclidean distance based Joint Domain Localized (E-JDL), is presented. Finally, the effectiveness of the proposed method is verified by experimental data and the results show that the method can improve the clutter suppression performance of STAP than traditional JDL method.