DocumentCode
3482697
Title
Optimisation of particle filters using simultaneous perturbation stochastic approximation
Author
Chan, Bao Ling ; Doucet, Arnaud ; Tadic, Vladislav B.
Author_Institution
Dept of Electr. & Electron. Eng., Univ. of Melbourne, Vic., Australia
Volume
6
fYear
2003
fDate
6-10 April 2003
Abstract
The paper addresses the optimisation of particle filtering methods aka sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using simultaneous perturbation stochastic approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.
Keywords
Monte Carlo methods; approximation theory; filtering theory; optimisation; perturbation techniques; sampling methods; stochastic processes; average cost function optimisation; data analysis; particle filters; random samples; sequential Monte Carlo methods; simultaneous perturbation stochastic approximation; Cost function; Filtering; Finite difference methods; Measurement standards; Optimization methods; Particle filters; Signal processing; Sliding mode control; State estimation; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
ISSN
1520-6149
Print_ISBN
0-7803-7663-3
Type
conf
DOI
10.1109/ICASSP.2003.1201773
Filename
1201773
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