Improved Multitarget Tracking Algorithm Based on Marginalized Particle Filter
SHI Zhiguo
WU Ming
HAO Yunpeng
SHI Donglei
Abstract：In the process of multi-target tracking,a new algorithm based on edge particle filter is proposed to solve the prob?lem that the probability density filter does not estimate the number of targets and the state of target correctly. Application of Rao-Blackwellized algorithm,the target state is decomposed into linear and non-linear model structure. And RBPF filter prediction is used to predict and estimatethe non-linear stateof target,and Calman filtering method is used to predict and estimate the linear state,in order to improve the estimation accuracy of target state,and the complexity of calculation is reduced. Finally,the simula?tion experiments are carried out to verify the proposed algorithm. Compared with the existing algorithms,the proposed algorithm can estimate the number of targets and thestate of target more accurately,and has better tracking performance.
Keywords：particle filterprobability hypothesis density filtermultitarget tracking
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 344-348 )