Adaptive kernels in approximate filtering of state-space models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00466448" target="_blank" >RIV/67985556:_____/17:00466448 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/acs.2739" target="_blank" >http://dx.doi.org/10.1002/acs.2739</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/acs.2739" target="_blank" >10.1002/acs.2739</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive kernels in approximate filtering of state-space models

Popis výsledku v původním jazyce

Standard Bayesian algorithms used for online filtering of states of hidden Markov models from noisy measurements assume an accurate knowledge of the measurement model in the form of a conditional probability density function. However, this knowledge is often unreachable in practice, and the used models are more or less misspecified, or it is too complex, making the resulting models intractable. This paper focuses on these issues from the particle filtering perspective. It adopts the principles of the approximate Bayesian filtering, where the particle weights are based on the (dis)similarity of the true measurements and the pseudo-measurements obtained by plugging the state particles directly into the measurement equation. Specifically, a new robust method for online tuning of the weighting kernel is proposed.

Název v anglickém jazyce

Adaptive kernels in approximate filtering of state-space models

Popis výsledku anglicky

Standard Bayesian algorithms used for online filtering of states of hidden Markov models from noisy measurements assume an accurate knowledge of the measurement model in the form of a conditional probability density function. However, this knowledge is often unreachable in practice, and the used models are more or less misspecified, or it is too complex, making the resulting models intractable. This paper focuses on these issues from the particle filtering perspective. It adopts the principles of the approximate Bayesian filtering, where the particle weights are based on the (dis)similarity of the true measurements and the pseudo-measurements obtained by plugging the state particles directly into the measurement equation. Specifically, a new robust method for online tuning of the weighting kernel is proposed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GP14-06678P" target="_blank" >GP14-06678P: Distribuované dynamické odhadování v difuzních sítích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

International Journal of Adaptive Control and Signal Processing

ISSN

0890-6327

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

938-952

Kód UT WoS článku

000403462300006

EID výsledku v databázi Scopus

2-s2.0-84997787314