Open AccessTarget tracking based on improved cubature particle filter in UWSNs
Author(s) -
Feng Hailin,
Cai Zhiwei
Publication year - 2019
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2018.5489
Subject(s) - particle filter , tracking (education) , resampling , kalman filter , fuse (electrical) , algorithm , computer science , filter (signal processing) , variance (accounting) , sensor fusion , mathematical optimization , mathematics , artificial intelligence , computer vision , engineering , psychology , pedagogy , accounting , electrical engineering , business
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.