A hybrid algorithm based on TDOA and DOA for underwater target localization

Abstract

To address the problems of complex scenarios, low accuracy of a single localization algorithm, and high requirements for localization system equipment, this paper proposes a Taylor-weighted least squares algorithm based on Time Differences of Arrival (TDOA) and Direction of Arrival (DOA) joint localization method. The method first performs DOA estimation of the target by the Bayesian algorithm with off-grid sparse reconstruction and converts the obtained orientation information into position coordinates, and finds the final position of the target through iterative operations. The proposed algorithm is compared with the existing TDOA method based on the Chan algorithm and the Taylor algorithm based on the TDOA with the Chan algorithm as the initial value and the Two-Step Weighted Least Squares (TSWLS) algorithm in a simulation environment. The results show that the proposed algorithm has better performance in terms of localization accuracy, noise immunity, stability and is suitable for target localization in underwater.