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

Abstract

Direction of arrival (DOA) estimation using sparsity-inducing techniques has attracted much interest recently. In this paper, the DOA estimation for 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. Firstly, the maximum a posteriori (MAP) estimation of 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. Simulation results show that the proposed methods outperform the conventional SBL methods, especially in harsh scenarios with limited number of snapshots and low signal-to-noise ratio (SNR).