Distributed bearing-only target tracking algorithm based on variational Bayesian inference under random measurement anomalies  

作　　者：YANG Haoran CHEN Yu HU Zhentao JIA Haoqian 杨浩然

机构地区：[1]School of Artificial Intelligence,Henan University,Zhengzhou 450046,P.R.China [2]Big Data Institute,Central Souch University,Changsha 410083,P.R.China

出　　处：《High Technology Letters》2025年第1期86-94,共9页高技术通讯(英文版)

基　　金：Supported by the Science and Technology Key Project of Science and Technology Department of Henan Province(No.252102211041);the Key Research and Development Projects of Henan Province(No.231111212500).

摘　　要：A distributed bearing-only target tracking algorithm based on variational Bayesian inference(VBI)under random measurement anomalies is proposed for the problem of adverse effect of random measurement anomalies on the state estimation accuracy of moving targets in bearing-only tracking scenarios.Firstly,the measurement information of each sensor is complemented by using triangulation under the distributed framework.Secondly,the Student-t distribution is selected to model the measurement likelihood probability density function,and the joint posteriori probability density function of the estimated variables is approximately decoupled by VBI.Finally,the estimation results of each local filter are sent to the fusion center and fed back to each local filter.The simulation results show that the proposed distributed bearing-only target tracking algorithm based on VBI in the presence of abnormal measurement noise comprehensively considers the influence of system nonlinearity and random anomaly of measurement noise,and has higher estimation accuracy and robustness than other existing algorithms in the above scenarios.

关 键 词：bearing-only target tracking(BOTT) variational Bayesian inference(VBI) Student-t distribution cubature Kalman filter(CKF) distributed fusion 

分 类 号：R74[医药卫生—神经病学与精神病学]