SPACE-TIME ADAPTIVE PROCESSING WITH MULTIPLE MEASUREMENT VECTORS BASED ON TSBL FOR AIRBORNE RADAR

Sparse recovery based space-time adaptive processing (SR-STAP) algorithms can achieve superior clutter suppression performance with limited training samples. However, most SR-STAP algorithms focus on modeling the sparsity of training samples from nearby range bins independently, ignoring the correlation between samples. In this paper, we devise a set of matrices to capture the correlations within the multiple measurement vectors (MMV), and find the optimal solution based on the temporal sparse Bayesian learning (TSBL) framework iteratively, called temporal MMV sparse Bayesian learning STAP algorithm (TMSBL-STAP). Moreover, we analyze the common off-grids problem in SR-STAP, and consider a prior dictionary that adds clutter ridge prior information to alleviate the off-grids problem. Numerical simulations are provided to validate the prior dictionary, and the effectiveness of the proposed algorithm is verified based on simulation and measured data.