Title :
A Maneuvering Target Tracking Method Based on RBPF
Author :
Xingxing Zou ; Yacong Zheng ; Xiaomeng Zhang ; Zengqiang Ma
Author_Institution :
Sch. of Electr. & Electron. Eng., Shijiazhuang Tiedao Univ., Shijiazhuang, China
Abstract :
The particle filtering (PF) is a widely used in present. However, as PF is used in nonlinear systems, a large number of particles are necessary to maintain high tracking accuracy and more computational burden are inevitably. Then, a new method of Rao-Blackwellised Particle (RBPF), in which Rao-Blackwell theorem is used to improve the performance of PF, is proposed in the paper. Firstly, nonlinear part and linear part of the maneuvering target tracking model are separated Based on Bayes principle. Then, the estimation of the linear part is dealed with by Kalman Filter (KF) and that of the nonlinear part by PF. The experiment results show that the tracking accuracy and the robustness of RBPF is higher than that of PF.
Keywords :
Bayes methods; Kalman filters; nonlinear systems; particle filtering (numerical methods); target tracking; Bayes principle; Kalman filter; RBPF; Rao-Blackwell theorem; Rao-Blackwellised particle; nonlinear systems; particle filtering; target tracking; tracking accuracy; Accuracy; Estimation; Maximum likelihood detection; Monte Carlo methods; Particle filters; Target tracking; KF; RBPF; particle filter; target tracking;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-0-7695-5011-4
DOI :
10.1109/IHMSC.2013.272
