An underwater moving dipole tracking method of artificial lateral line based on intelligent optimization and recursive filter

Abstract

Inspired by the lateral line system of fish, an artificial lateral line system is proposed for underwater target detection. The dipole is treated as a standard and simplified target. In previous studies, most researchers focused on the dipole at a fixed position and the trajectory tracking of a moving dipole was barely considered. In this paper, a new trajectory tracking method for a moving dipole is proposed. First, based on the instant pressure amplitude and loss function, the dipole trajectory is tracked by particle swarm optimization (PSO). Then, the PSO-tracked trajectory is optimized by using recursive filters such as a Kalman filter (KF) and a particle filter (PF) to reduce the tracking error. The experiment result showed that when the trajectory of the dipole was rectangular, the target tracking accuracy of PSO was competitive compared with the Gauss–Newton method. The mean error distance (MED) of PSO was 12.51 mm. The PF showed better optimization performance than the KF in this study, and the corresponding MED of the PF was 7.064 mm. The main factor that caused tracking errors was pressure mismatch. In the simulation, when pressure mismatch was not considered, the performance of the proposed dipole tracking method was highly improved.