A fast underwater acoustic target direction of arrival estimation method based on sparse Bayesian learning

The direction of arrival(DOA) estimation methods for underwater acoustic target which using temporally multiple sparse Bayesian learning(TMSBL) as the reconstructing algorithm,were slower for the process of computation.To solve this problem,a fast underwater acoustic target direction of arrival estimation based on sparse Bayesian learning research was proposed.Analyzing the model characteristics of block-spare Bayesian learning framework for DOA,the algorithm proposed to obtain the value of core hyper-parameter through MacKay's fixed-point method to estimate the DOA,so it spent less time for computation,and had more superior recovery performance than TMSBL algorithm.Simulation results verified the feasibility and effectiveness of the proposed algorithm by comparing the DOA estimation performance with TMSBL and other traditional DOA algorithms through different facets such as operation time,failure rate and root mean square error.