Double layer weighted unscented Kalman underwater target tracking algorithm based on sound speed profile

Abstract

For target tracking in underwater wireless sensor networks, the inhomogeneous medium refracts the sound waves and bends their propagation path, which reduces the measurement accuracy of sensor nodes. In addition, since the information measured by nodes are different, modification effects of nodes to the predicted states of tracking algorithm are variable. Based on the problems, this paper constructs Fisher information matrix to weight target state information contained in nodes using acoustic time-of-flight as the measurements. With the information-weighted measurements, we propose a double layer weighted unscented Kalman tracking algorithm based on sound speed profile (SSP_DLWUKF). SSP_DLWUKF consists of outer layer unscented Kalman filter (UKF) algorithm and inner layer UKF algorithm. The inner layer UKF is used to predict target state, update the weights of the outer layer particles and weight the measurements. The outer layer UKF is utilized to modify target states predicted by inner layer UKF. The simulation results show that the mean tracking error of SSP_DLWUKF is about 55.81% of that of double layer unscented Kalman filter (DLUKF) algorithm, 71.29% that of UKF and 50.58% that of particle filter (PF). Simulation and experiment demonstrate that SSP_DLWUKF can utilize measurements to effectively reduce tracking errors and improve tracking accuracy.