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

Result code in 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>
Result on the web
<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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Result language
angličtina
Original language name
Sigma-Point Set Rotation for Derivative-Free Filters in Target Tracking Applications
Original language description
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.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20205 - Automation and control systems

Project
<a href="/en/project/GA15-12068S" target="_blank" >GA15-12068S: Adaptive Approaches to State Estimation of Nonlinear Stochastic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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