摘要
Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking(MTT) system,a new Gaussian-Student’s t mixture distribution probability hypothesis density(PHD) robust filtering algorithm based on variational Bayesian inference(GST-vbPHD) is proposed.Firstly,since it can accurately describe the heavy-tailed characteristics of noise with outliers,Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively.Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability,leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise.Finally,the approximate solutions including target weights,measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach.The simulation results show that,in the heavy-tailed noise environment,the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter.
作者
胡振涛
YANG Linlin
HU Yumei
YANG Shibo
HU Zhentao;YANG Linlin;HU Yumei;YANG Shibo(School of Artificial Intelligence,Henan University,Zhengzhou 450046,P.R.China;School of Automation,Northwestern Polytechnical University,Xi'an 710029,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 Technology Department of Henan Province(No.212102310298)
the Innovation and Quality Improvement Project for Graduate Education of Henan University(No.SYL20010101)。