Fast Estimation of Direction of Arrival for Towed Array Based on Sparse Bayesian Learning

Abstract

In order to solve the problem of slow convergence of the direction of arrival (DOA) estimation algorithm based on sparse Bayesian learning (SBL), a fast converging SBL(FCSBL) of DOA estimation algorithm is obtained by introducing an approximate posterior covariance in hyperparameter iteration. During maneuvering turns, the towed array is modeled as a parabolic array to correct the distortion of array shape. Taking the bow of the array as a hyperparameter for SBL, this paper proposes a fast converging adaptive bow sparse Bayesian learning algorithm, to jointly estimate array shape and DOAs from acoustic data. Numerical simulation and MAPEX2000 experimental data processing results show that FC-ABSBL performs well in detection of weak targets and estimation of the array bow during maneuvering turns with low computational load.