Clutter Reduced-Dimension Sparse Recovery Method on Knowledge-Aided for Airborne Phased Array Radar

To reduce the computational load of the typical sparse recovery space-time adaptive processing (SR-STAP), a novel reduced-dimension SR method on knowledge-aided is proposed. According to the clutter distribution in the spatial-temporal plane, partial grid points near the clutter bridge are first selected to form a subset as prior knowledge. Then, clutter data on each grid point from SR dictionary are denoted and correlated coefficients between the grid data from the prior subset and that from the SR dictionary are defined. Lastly, the threshold about SR reduced-dimension is designed and the reduced-dimension dictionary is constructed. Monte Carlo simulation results show the proposed method reduces the computational burden significantly and has the highly similar clutter suppression performance with the typical SR-STAP.