Multi-Model Shrinkage for Knowledge STAP in High Frequency Surface-wave Radars

Space-time adaptive processing (STAP) refers to the extension of adaptive antenna techniques has been increasingly applied to suppress interference signals (clutter and jamming). However, the sea clutter mainly limits the target detection for high frequency surface wave radars (HFSWRs). Because the interference is unknown a priori, it must be estimated adaptively from the finite amount of data comprising the coherent processing interval (CPI). In this paper, we applied multi-model shrinkage estimator for knowledge-aided STAP for real sea clutter data gathered by a HFSWR. It offers to estimate the clutter covariance from training data. The performance of this approach was studiedwith KASSPER I data set and simulation before. We evaluate the performance of it as ROC curve. It results the number of pulses and training data for operation in the sea environment with HFSWRs

کلیدواژه ها:

STAP ، Sea clutter ، covariance matrix estimation ، HFSWR