DocumentCode :
3241634
Title :
Convergence Properties of Particle Filter Algorithm
Author :
Qu, Yanwen ; Chen, Yi ; Yang, Jingyu
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing
fYear :
2008
fDate :
22-24 Oct. 2008
Firstpage :
1
Lastpage :
6
Abstract :
The basic sampling importance resampling algorithm is the basic for improving particle filter methods which are widely utilized in optimal filtering problems. In our paper, we introduce a modified basic SIR algorithm and analyze the convergence property of the modified basic SIR algorithm. Furthermore, when the recursive time is finite and the forth-order moment of the interesting function w.r.t the posterior joint distribution of the extended state is exist, the sufficient condition for the basic particle filter estimation convergence almost surely to the optimal estimation is discussed.
Keywords :
importance sampling; particle filtering (numerical methods); convergence property; particle filter algorithm; posterior joint distribution; sampling importance resampling algorithm; Computer science; Convergence; Electronic mail; Filtering algorithms; Monte Carlo methods; Particle filters; Recursive estimation; Sampling methods; State estimation; Yttrium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. CCPR '08. Chinese Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2316-3
Type :
conf
DOI :
10.1109/CCPR.2008.14
Filename :
4662967
Link To Document :
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