A fast sparse Bayesian learning method with adaptive Laplace prior for space‐time adaptive processing

Space‐time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse Bayesian learning algorithm and the adaptive least absolute shrinkage and selection operator algorithm, a hierarchical Bayesian framework with adaptive Laplace priors is proposed. In this paper, a novel method is applied to avoid the high‐dimension matrix inverse operation in the proposed algorithm. Moreover, in order to apply the method in the complex‐valued domain, the complex‐valued signal is split into two independent variables. Then, the sparse recovery problem in the complex‐valued domain can be transformed into the real‐value domain. Simulation experiments show that the proposed algorithm can achieve great clutter suppression performance and also ensure high computational efficiency.