Particle filter with adaptive sample size
Ondřej Straka; Miroslav Šimandl
Kybernetika (2011)
- Volume: 47, Issue: 3, page 385-400
- ISSN: 0023-5954
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topStraka, Ondřej, and Šimandl, Miroslav. "Particle filter with adaptive sample size." Kybernetika 47.3 (2011): 385-400. <http://eudml.org/doc/196975>.
@article{Straka2011,
abstract = {The paper deals with the particle filter in state estimation of a discrete-time nonlinear non-Gaussian system. The goal of the paper is to design a sample size adaptation technique to guarantee a quality of a filtering estimate produced by the particle filter which is an approximation of the true filtering estimate. The quality is given by a difference between the approximate filtering estimate and the true filtering estimate. The estimate may be a point estimate or a probability density function estimate. The proposed technique adapts the sample size to keep the difference within pre-specified bounds with a pre-specified probability. The particle filter with the proposed sample size adaptation technique is illustrated in a numerical example.},
author = {Straka, Ondřej, Šimandl, Miroslav},
journal = {Kybernetika},
keywords = {stochastic systems; nonlinear filtering; particle filter; sample size; adaptation; stochastic systems; nonlinear filtering; particle filter; sample size; adaptation},
language = {eng},
number = {3},
pages = {385-400},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Particle filter with adaptive sample size},
url = {http://eudml.org/doc/196975},
volume = {47},
year = {2011},
}
TY - JOUR
AU - Straka, Ondřej
AU - Šimandl, Miroslav
TI - Particle filter with adaptive sample size
JO - Kybernetika
PY - 2011
PB - Institute of Information Theory and Automation AS CR
VL - 47
IS - 3
SP - 385
EP - 400
AB - The paper deals with the particle filter in state estimation of a discrete-time nonlinear non-Gaussian system. The goal of the paper is to design a sample size adaptation technique to guarantee a quality of a filtering estimate produced by the particle filter which is an approximation of the true filtering estimate. The quality is given by a difference between the approximate filtering estimate and the true filtering estimate. The estimate may be a point estimate or a probability density function estimate. The proposed technique adapts the sample size to keep the difference within pre-specified bounds with a pre-specified probability. The particle filter with the proposed sample size adaptation technique is illustrated in a numerical example.
LA - eng
KW - stochastic systems; nonlinear filtering; particle filter; sample size; adaptation; stochastic systems; nonlinear filtering; particle filter; sample size; adaptation
UR - http://eudml.org/doc/196975
ER -
References
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