DocumentCode :
2411024
Title :
An adaptive nonparametric particle filter for state estimation
Author :
Wang, Yali ; Chaib-draa, Brahim
Author_Institution :
Dept. of Comput. Sci. & Software Eng., Laval Univ., Quebec City, QC, Canada
fYear :
2012
fDate :
14-18 May 2012
Firstpage :
4355
Lastpage :
4360
Abstract :
Particle filter is one of the most widely applied stochastic sampling tools for state estimation problems in practice. However, the proposal distribution in the traditional particle filter is the transition probability based on state equation, which would heavily affect estimation performance in that the samples are blindly drawn without considering the current observation information. Additionally, the fixed particle number in the typical particle filter would lead to wasteful computation, especially when the posterior distribution greatly varies over time. In this paper, an advanced adaptive nonparametric particle filter is proposed by incorporating gaussian process based proposal distribution into KLD-Sampling particle filter framework so that the high-qualified particles with adaptively KLD based quantity are drawn from the learned proposal with observation information at each time step to improve the approximation accuracy and efficiency. Our state estimation experiments on univariate nonstationary growth model and two-link robot arm show that the adaptive nonparametric particle filter outperforms the existing approaches with smaller size of particles.
Keywords :
Gaussian processes; particle filtering (numerical methods); sampling methods; state estimation; Gaussian process; KLD-sampling particle filter framework; adaptive nonparametric particle filter; proposal distribution; state equation; state estimation; stochastic sampling tools; transition probability; two-link robot arm; univariate nonstationary growth model; Approximation methods; Gaussian processes; Particle filters; Proposals; Robots; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2012 IEEE International Conference on
Conference_Location :
Saint Paul, MN
ISSN :
1050-4729
Print_ISBN :
978-1-4673-1403-9
Electronic_ISBN :
1050-4729
Type :
conf
DOI :
10.1109/ICRA.2012.6224840
Filename :
6224840
Link To Document :
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