A robust refined training sample reweighting space–time adaptive processing method for airborne radar in heterogeneous environment

To improve the clutter suppression performance of airborne radar in heterogeneous environment, a robust refined training sample reweighting space–time adaptive processing (STAP) method called RRSRW is proposed here. First, some target-free samples around cell under test (CUT) are selected and corresponding dictionary matrices constructed using system parameters. Then, patch amplitudes array error for simultaneously estimated through formulated constrained least squares problem. Subsequently, based on covariance matching estimation criterion, local weighting coefficients by redesigned convex optimisation Finally, STAP weight vector calculated to process CUT data. The can effectively protect moving targets data, which free hyper-parameters has global convergence properties. Simulation results demonstrate that suppress strong ground greatly detection environment.