A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU122022" target="_blank" >RIV/00216305:26220/16:PU122022 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ISSPIT.2016.7886040" target="_blank" >http://dx.doi.org/10.1109/ISSPIT.2016.7886040</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISSPIT.2016.7886040" target="_blank" >10.1109/ISSPIT.2016.7886040</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

Popis výsledku v původním jazyce

The identification of slowly-varying noise parameters in non-linear state-space models constitutes a long-standing problem. The present paper addresses this task using the Bayesian framework and sequential Monte Carlo (SMC) methodology. The proposed approach utilizes an algebraic structure of the model so that the Rao-Blackwellization of the parameters can be performed, thus involving a finite-dimensional sufficient statistic for each particle trajectory into the resulting algorithm. However, relying on standard SMC methods, such techniques are known to suffer from the particle path degeneracy problem. To counteract this issue, it is proposed to use alternative stabilized forgetting, which compensates for the incomplete knowledge of a model of parameter variations by finding a compromise between possible predictive densities of the parameters. An experimental study proves the efficiency of the introduced Rao-Blackwellized particle filter (RBPF) compared to some recently proposed approaches.

Název v anglickém jazyce

A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

Popis výsledku anglicky

The identification of slowly-varying noise parameters in non-linear state-space models constitutes a long-standing problem. The present paper addresses this task using the Bayesian framework and sequential Monte Carlo (SMC) methodology. The proposed approach utilizes an algebraic structure of the model so that the Rao-Blackwellization of the parameters can be performed, thus involving a finite-dimensional sufficient statistic for each particle trajectory into the resulting algorithm. However, relying on standard SMC methods, such techniques are known to suffer from the particle path degeneracy problem. To counteract this issue, it is proposed to use alternative stabilized forgetting, which compensates for the incomplete knowledge of a model of parameter variations by finding a compromise between possible predictive densities of the parameters. An experimental study proves the efficiency of the introduced Rao-Blackwellized particle filter (RBPF) compared to some recently proposed approaches.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 16th International Symposium on Signal Processing and Information Technology, ISSPIT 2016

ISBN

978-1-5090-5844-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

229-234

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Limassol

Místo konání akce

Limassol

Datum konání akce

12. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000406122500042