Tracking an underwater maneuvering target using an adaptive Kalman filter

Abstract

To improve the tracking accuracy of an underwater maneuvering target, according to its characteristics of low speed and weak maneuvering performance, an adaptive Kalman filter is given based on the online estimation of the process noise variance. As the main filter analyzes the target motion, the process noise variance of the main filter is estimated by an auxiliary filter for being adaptively adjusted according to the target maneuvering intensity to improve the target tracking accuracy for uniform motions, as well as improving response speed of the filter for maneuvering behavior of the target. Simulation results show that the proposed algorithm performs well, which, to a certain extent, effectively improves the tracking accuracy of an underwater maneuvering target.