Target tracking based on improved cubature particle filter in UWSNs

Abstract

In this study, an improved cubature particle filter based on the artificial bee colony (ABC) algorithm is proposed and applied to target tracking via underwater wireless sensor networks (UWSNs). In the proposed method, the square root cubature Kalman filter is used to generate the proposal distribution and the ABC algorithm is employed to optimise the particles before resampling, which makes the particles move toward the high likelihood region and maintain the diversity of the particles. Moreover, linear minimum variance criterion is utilised to fuse local estimates together in distributed fusion architectures of UWSNs. The simulation results show that the proposed method outperforms other classical algorithms in tracking accuracy.