Abstract
High-frequency surface wave radar (HFSWR) often suffers from highly non-uniform, striped ionospheric clutter, which significantly degrades sea-surface target detection performance. To address this challenge, this paper proposes a reduced-dimension space-time adaptive processing (STAP) algorithm based on sparse representation. In this method, a dictionary is first constructed using the Doppler resolution and an appropriate angle interval as the frequency and angle grids, aiming to obtain fully orthogonal clutter atoms. Both the training sample and the cell under test are then sparsely represented over this dictionary to extract consistent clutter atoms. Due to discrepancies between the dictionary atoms and the actual clutter, low-power atoms are deemed unreliable and are discarded via a thresholding procedure. The remaining reliable atoms are used to construct a dimensionality-reduction matrix, thereby obtaining an accurate local clutter-plus-noise covariance matrix. Experimental results on measured data demonstrate that the proposed method effectively suppresses striped ionospheric clutter and enhances target detection performance.