State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties
State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-me...
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| Main Author: | |
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| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
KIT Scientific Publishing
2014
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| Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 100 | 1 | |a Noack, Benjamin |4 auth | |
| 245 | 1 | 0 | |a State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties |
| 260 | |b KIT Scientific Publishing |c 2014 | ||
| 300 | |a 1 electronic resource (XVIII, 257 p. p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory | |
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-sa/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-sa/4.0/ | ||
| 546 | |a English | ||
| 653 | |a distributed estimation | ||
| 653 | |a Kalman filter | ||
| 653 | |a set-membership estimation | ||
| 653 | |a Bayesian state estimation | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://www.ksp.kit.edu/9783731501244 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/59976 |7 0 |z DOAB: description of the publication |