Methods of target motion estimation for AUV target tracking

Abstract

When the autonomous underwater vehicle (AUV) perform underwater target tracking tasks, it is hard to guarantee the tracking accuracy because of the complexity and changefulness of the motion of the target. To overcome this problem, the unscented Kalman filter (UKF) and particle filter (PF) is designed by using Kalman filtering and particle filtering method, and both the typical motion model and nonlinear stochastic motion model of the target has been established. Now by analyzing the data collected from the forward looking sonar, the motion track can be estimated by the two filters. Finally, the simulation experiments for the nonlinear random motion have been conducted based on Matlab respectively, the results show that the estimation accuracy of UKF and PF is significantly higher that of EKF, thus the validity of the estimation of target motion by using the unscented Kalman filter and particle filter is verified.