Title :
Particle Filter Based on Strong Tracking Filter
Author :
Deng, Xiao-Long ; Guo, Wei-Zhong ; Xie, Jian-Yin ; Liu, Jun
Abstract :
One of the key issues for particle filter is the proposal distribution. A new proposal distribution, the strong tracking filter (STF) proposal distribution, is presented. The time-varied fading factor in the STF that can be tuned on line makes the algorithm adaptive. In the bearings-only passive target tracking examples, the simulation results confirm the efficiency of particle filter with the new proposal distribution.
Keywords :
particle filter; proposal distribution; strong tracking filter; target tracking; Adaptive filters; Density functional theory; Density measurement; Filtering theory; Gaussian processes; Mechanical engineering; Particle filters; Particle tracking; Proposals; Target tracking; particle filter; proposal distribution; strong tracking filter; target tracking;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527026
