DocumentCode :
3431763
Title :
A biological inspired improvement strategy for Particle Filters
Author :
Zhong, J.P. ; Fung, Y.F.
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech. Univ., Hong Kong
fYear :
2009
fDate :
10-13 Feb. 2009
Firstpage :
1
Lastpage :
6
Abstract :
Particle filters (PF) is a model estimation technique based on simulation. But two problems, namely particle impoverishment and sample size dependency, frequently occur during the particle updating stage and these problems will reduce the accuracy of the estimation results. In order to avoid these problems, ant colony optimization is incorporated into the generic particle filter before the updating stage. After the optimization, particle samples will move closer to their local highest posterior density function and better estimation results can be produced.
Keywords :
optimisation; particle filtering (numerical methods); state estimation; ant colony optimization; biological inspired improvement strategy; local highest posterior density function; model estimation technique; particle filters; Ant colony optimization; Bayesian methods; Biological system modeling; Monte Carlo methods; Particle filters; Probability distribution; Recursive estimation; Sampling methods; State estimation; State-space methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 2009. ICIT 2009. IEEE International Conference on
Conference_Location :
Gippsland, VIC
Print_ISBN :
978-1-4244-3506-7
Electronic_ISBN :
978-1-4244-3507-4
Type :
conf
DOI :
10.1109/ICIT.2009.4939539
Filename :
4939539
Link To Document :
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