Sigma-Point Set Rotation for Derivative-Free Filters in Target Tracking Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43929108" target="_blank" >RIV/49777513:23520/16:43929108 - isvavai.cz</a>

Výsledek na webu

<a href="http://isif.org/journal/11/1/1557-6418" target="_blank" >http://isif.org/journal/11/1/1557-6418</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Sigma-Point Set Rotation for Derivative-Free Filters in Target Tracking Applications

Popis výsledku v původním jazyce

The paper focuses on the state estimation of the nonlinear discrete-time stochastic dynamic systems by the derivative-free filters. In particular the impact of the sigma-point set rotation on the performance of the unscented transform and the unscented Kalman filter (UKF) is analysed. It is shown that the sigma-point set rotation is an additional user-defined parameter closely tied with the covariance matrix decomposition technique used in sigma-point computation that significantly affects the estimation performance. Analysis, algorithms, and recommendations for computations of the optimal sigma-point set rotation are provided to determine either the rotation prior to the estimation experiment (off-line) or during the estimation experiment (on-line). Further, two approaches for a reduction of optimization computational costs are presented. The proposed algorithms, namely the on-line adaptive-sigma-point-set-UKF (AUKF) and off-line trained-sigma-point-set-UKF (TUKF), are illustrated and verified in a numerical study considering two static and two dynamic examples. The TUKF improves the UKF performance, while the computational complexity is preserved. The AUKF further improves the estimate accuracy with increased computational burden.

Název v anglickém jazyce

Sigma-Point Set Rotation for Derivative-Free Filters in Target Tracking Applications

Popis výsledku anglicky

The paper focuses on the state estimation of the nonlinear discrete-time stochastic dynamic systems by the derivative-free filters. In particular the impact of the sigma-point set rotation on the performance of the unscented transform and the unscented Kalman filter (UKF) is analysed. It is shown that the sigma-point set rotation is an additional user-defined parameter closely tied with the covariance matrix decomposition technique used in sigma-point computation that significantly affects the estimation performance. Analysis, algorithms, and recommendations for computations of the optimal sigma-point set rotation are provided to determine either the rotation prior to the estimation experiment (off-line) or during the estimation experiment (on-line). Further, two approaches for a reduction of optimization computational costs are presented. The proposed algorithms, namely the on-line adaptive-sigma-point-set-UKF (AUKF) and off-line trained-sigma-point-set-UKF (TUKF), are illustrated and verified in a numerical study considering two static and two dynamic examples. The TUKF improves the UKF performance, while the computational complexity is preserved. The AUKF further improves the estimate accuracy with increased computational burden.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA15-12068S" target="_blank" >GA15-12068S: Adaptivní přístupy k odhadu stavu nelineárních stochastických systémů</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Journal of Advances in Information Fusion

ISSN

1557-6418

e-ISSN

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Svazek periodika

11

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

91-109

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85006161638