A robust sparsity-based tri-iterative space-time adaptive processing method for airborne radar

To enhance the system performance of airborne radar in a limited-sample environment, a robust sparsity-based tri-iterative space-time adaptive processing (RSBTI-STAP) method is proposed here. First, the proposed RSBTI-STAP method constructs the clutter dictionary matrix by exploiting the a priori knowledge of radar platform. Next, the clutter representation coefficient, array error and weighted vectors are iteratively updated until convergence so as to obtain the estimated values of these three parameters. Finally, combined with the estimated clutter representation coefficient and array error vectors, the STAP weight vector is calculated for the purpose of clutter suppression. The proposed method can be robust to the array amplitude/phase error and can be free of the hyperparameters. Additionally, it does not need the training samples and has the global convergent properties. Numerical experiments based on the simulated data demonstrate the effectiveness of the proposed RSBTI-STAP method.