Design of Rao-Blackwellized Point-Mass Smoother for Conditionally Linear and Gaussian Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956257" target="_blank" >RIV/49777513:23520/19:43956257 - isvavai.cz</a>

Výsledek na webu

<a href="https://dx.doi.org/10.1109/TSP.2019.2949506" target="_blank" >https://dx.doi.org/10.1109/TSP.2019.2949506</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2019.2949506" target="_blank" >10.1109/TSP.2019.2949506</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of Rao-Blackwellized Point-Mass Smoother for Conditionally Linear and Gaussian Models

Popis výsledku v původním jazyce

This paper deals with the state estimation of nonlinear stochastic dynamic systems. Stress is laid on the numerical solution to the functional recursive relations providing conditional probability density functions of the state. In particular, a novel Rao-Blackwellized point-mass smoother with two implementations is proposed for conditionally linear and Gaussian state-space models, where part of the state vector is estimated by a computationally expensive point-mass smoother, whereas the remaining part of the state vector by a set of computationally efficient linear smoothers. Such decomposition results in a computationally less demanding smoother than the standard point-mass smoother for the considered models. The properties of the proposed smoother are discussed and its performance is numerically illustrated.

Název v anglickém jazyce

Design of Rao-Blackwellized Point-Mass Smoother for Conditionally Linear and Gaussian Models

Popis výsledku anglicky

This paper deals with the state estimation of nonlinear stochastic dynamic systems. Stress is laid on the numerical solution to the functional recursive relations providing conditional probability density functions of the state. In particular, a novel Rao-Blackwellized point-mass smoother with two implementations is proposed for conditionally linear and Gaussian state-space models, where part of the state vector is estimated by a computationally expensive point-mass smoother, whereas the remaining part of the state vector by a set of computationally efficient linear smoothers. Such decomposition results in a computationally less demanding smoother than the standard point-mass smoother for the considered models. The properties of the proposed smoother are discussed and its performance is numerically illustrated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

67

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

6053-6066

Kód UT WoS článku

000499985600002

EID výsledku v databázi Scopus

2-s2.0-85075644300