An efficient real-valued sparse Bayesian learning for non-circular signal's DOA estimation in the presence of impulsive noise

Abstract Currently, sparse Bayesian learning (SBL) has been introduced to solve direction of arrival (DOA) estimation in different situations. In the line of DOA estimation under impulsive noise, existing SBL-based methods need large computation which will restrict their practicabilities. To address this problem, we propose an efficient method based on a real-valued SBL for non-circular signals in this paper. Firstly, received signal model is transformed into a real-valued form using the characteristic of non-circular signals' structure. Then, a sparse representation of the modified signal model is constructed in the presence of impulsive noise. Finally, SBL is applied to reconstruct the real-valued sparse model and solve the DOAs estimation. A series of simulations are carried out in different conditions to evaluate the proposed method. Simulation results demonstrate that our method shows better performance than existing methods.