TY - GEN
T1 - A new particle filtering algorithm with structurally optimal importance function
AU - Ait-El-Fquih, Boujemaa
AU - Deshouvries, François
PY - 2008
Y1 - 2008
N2 - Bayesian estimation in nonlinear stochastic dynamical systems has been addressed for a long time. Among other solutions, Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure. However, a drawback of the classical PF algorithms is that the optimal conditional importance distribution (CID) is often difficult (or even impossible) to compute and to sample from. As a consequence, suboptimal sampling strategies have been proposed in the literature. In this paper we bypass this difficulty by rather considering the prediction sequential importance sampling (SIS) problem; the filtering MC approximation is obtained as a byproduct. The advantage of this prediction-PF method is that it combines optimality and simplicity, since for the prediction problem, the optimal CID happens to be the prior transition of the underlying Markov Chain (MC), from which it is often simple to sample from.
AB - Bayesian estimation in nonlinear stochastic dynamical systems has been addressed for a long time. Among other solutions, Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure. However, a drawback of the classical PF algorithms is that the optimal conditional importance distribution (CID) is often difficult (or even impossible) to compute and to sample from. As a consequence, suboptimal sampling strategies have been proposed in the literature. In this paper we bypass this difficulty by rather considering the prediction sequential importance sampling (SIS) problem; the filtering MC approximation is obtained as a byproduct. The advantage of this prediction-PF method is that it combines optimality and simplicity, since for the prediction problem, the optimal CID happens to be the prior transition of the underlying Markov Chain (MC), from which it is often simple to sample from.
KW - Hidden Markov chains
KW - Optimal importance function
KW - Particle filtering
KW - Sequential importance sampling
UR - http://www.scopus.com/inward/record.url?scp=51449096964&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4518384
DO - 10.1109/ICASSP.2008.4518384
M3 - Conference contribution
AN - SCOPUS:51449096964
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3413
EP - 3416
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -