Fast Variational Bayesian Inference for Space-Time Adaptive Processing

Abstract

Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be substantially reduced. Experiments conducted on the simulated data and measured data verify that the proposed algorithm has excellent clutter suppression and target detection performance.