Direction of Arrival Estimation via Joint Sparse Bayesian Learning for Bi-Static Passive Radar

Direction-of-arrival (DOA) estimation using sparsity-inducing techniques has attracted much interest recently. In this paper, the DOA estimation for the bi-static passive radar is investigated. Under the framework of sparse Bayesian learning (SBL), a joint sparse Bayesian model is established to combine the measurements from both stations and yield improved targets DOA estimation. First, the maximum a posteriori (MAP) estimation of the DOA using the joint data set is derived. With the utilization of more measurements, the joint reconstruction process can produce far more precise estimates. To reduce the computational expense, a fast SBL method based on evidence maximization is also proposed. Using the fix-point method, the fast SBL method tends to converge faster than the MAP estimator. Theoretical results focusing on local convergence property of the fast SBL method are provided. The simulation results show that the proposed methods outperform the conventional SBL methods, especially in harsh scenarios with a limited number of snapshots and low signal-to-noise ratio (SNR).