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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1201773
