Reduced-Dimension STAP Algorithm Based on Local Space Time Coupling Characteristics

Patchy ionospheric clutter in high frequency surface wave radar (HFSWR) exhibits significant non-uniformity and local space time coupling characteristics. However, the best channel method (BCM) algorithm based on the maximum output signal-to-clutter-plus-noise ratio (SCNR) criterion suffers from inaccurate estimation of the clutter-plus-noise covariance matrix under non-homogeneous conditions. In addition, the reduced-dimension clutter subspace does not lie on a single clutter ridge, leading to performance degradation of the space time adaptive processing (STAP) algorithm. To address these issues, this paper proposes a reduced-dimension STAP algorithm based on local space time coupling characteristics. First, a sparse representation method is employed to reconstruct the clutter covariance matrix to improve estimation accuracy. Then, sparse representation is performed in the region near the target to extract candidate clutter channels. Based on the maximum output SCNR criterion, a bilateral alternating channel removal strategy is used to iteratively eliminate clutter channels, while a stopping condition is set to progressively approach a single clutter ridge. Finally, considering the errors introduced by sparse representation and iterative processing, a least-squares method is applied to fit the clutter ridge, and reduced-dimension channels are constructed along the fitted ridge. Experimental results based on measured data demonstrate the effectiveness of the proposed algorithm.