Title: On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning

Author(s): WANG Biao; ZHU Zhihui; DAI Yuewei;

Affiliation(s): School of Electronic and Information, Jiangsu University of Science and Technology, et al.

Abstract: The Direction of Arrival(DOA) estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning(TMSBL) as the reconstructing algorithm have the disadvantage of slow computing speed.To solve this problem,a fast underwater acoustic target direction of arrival estimation was proposed.Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation,an algorithm was proposed to obtain the value of core hyper-parameter through MacKay's fixed-point method to estimate the DOA.By this process,it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm.Simulation results verified the feasibiUty and effectiveness of the proposed algorithm.