摘要
A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses.Firstly,the Inverse-Wishart(IW)distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework.Secondly,the Bernoulli random variable is introduced as the judgement factor of the measurement losses,and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form.Finally,the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference,and the fixed-point iteration approach is used to update the estimated variables.The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity,time-varying measurement noise and unknown measurement loss probability,moreover,effectively improves the accuracy of target state estimation in complex scene.
作者
HU Zhentao
JIA Haoqian
GONG Delong
胡振涛;JIA Haoqian;GONG Delong(School of Artificial Intelligence,Henan University,Zhengzhou 450046,P.R.China;Laboratory and Equipment Management Office,Henan University,Zhengzhou 450046,P.R.China)
基金
Supported by the National Natural Science Foundation of China(No.61976080)
the Science and Technology Key Project of Science and TechnologyDepartment of Henan Province(No.212102310298)
the Academic Degrees&Graduate Education Reform Project of Henan Province(No.2021SJGLX195Y)
the Innovation and Quality Improvement Project for Graduate Education of Henan University(No.SYL20010101)。
