Fast Estimation of Direction of Arrival for Towed Array Based on Sparse Bayesian Learning
Zican Zhang, Xiang Pan · 2023
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.