Title :
An improvement to the linear jump Markov system Gaussian mixture probability hypothesis density filter for maneuvering target tracking
Author :
Shicang, Zhang ; Jianxun, Li ; Liangbin, Wu
Author_Institution :
Autom. Dept., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
An improvement approach to the linear Gaussian jump Markov system (LGJMS) Gaussian Mixture probability hypothesis density (GM-PHD) filter is designed for multiple maneuvering targets tracking. This method, which called mixture LGJMS GM-PHD (MLGJMS GM-PHD) filter, is based on the theory of generalized psuedo Bayes of the first order after the update step of LGJMS GM-PHD. Compared with the existing LGJMS GM-PHD filter, simulation results show that the designed filter weights over the original one.
Keywords :
Bayes methods; Gaussian processes; Markov processes; target tracking; generalized pseudo Bayes method; linear jump Markov system Gaussian mixture probability hypothesis density filter; mixture LGJMS GM-PHD filter; multiple maneuvering target tracking; Adaptation models; Bayesian methods; Markov processes; Radar tracking; Simulation; Target tracking;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-2118-2
DOI :
10.1109/ICIEA.2012.6361021
