Clutter suppression based on iterative reweighted methods with multiple measurement vectors for airborne radar

Abstract

Sparse recovery algorithms have been applied to the Space-time adaptive processing for reducing the requirement of samples over the past 15 years. However, many Sparse recovery algorithms are not robust and need accurate user parameters. Conventional sparse Bayesian learning (SBL) algorithms are insensitivity to user parameters but converge slowly. To remedy the limitation, two iterative reweighted algorithms are proposed based on SBL. In order to minimise the SBL penalty function, we construct its upper-bounding surrogate function via the concave conjugate function and apply iterative reweighted algorithms to minimise the surrogate function. Theoretical analysis and numerical experiments all exhibit great performance of the proposed algorithms.