State-updating-based DOA estimation using sparse Bayesian learning

Abstract

Recent studies of the direction-of-arrival (DOA) estimation reveal that the methods based on sparse Bayesian learning (SBL) exhibit many advantages over conventional approaches. However, these methods still face difficulties in practical applications due to their high computational complexity. To address the problem, a state-updating-based DOA estimation method using sparse Bayesian learning is proposed in this paper. In the proposed method, the state filter is employed to establish a recursive link among the source states within the observation time, and a probability distribution-based fitting technique is developed to fit the initial values for the iterative process by the source states. Numerical simulations and experimental results demonstrate that, the proposed method yields significantly reduced computational complexity and improved DOA estimation accuracy.