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dc.contributor.author
Yong, Sze Zheng
dc.contributor.author
Zhu, Minghui
dc.contributor.author
Frazzoli, Emilio
dc.date.accessioned
2021-01-08T17:52:09Z
dc.date.available
2020-11-26T04:36:24Z
dc.date.available
2020-11-30T15:44:10Z
dc.date.available
2021-01-08T17:52:09Z
dc.date.issued
2021-01-25
dc.identifier.issn
1049-8923
dc.identifier.issn
1099-1239
dc.identifier.other
10.1002/rnc.5306
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/453103
dc.description.abstract
In this paper, we propose a filtering algorithm for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. Using a multiple‐model approach with a bank of linear input and state filters for each mode, our algorithm relies on the ability to find the most probable model as a mode estimate, which we show is possible with input and state filters by identifying a key property, that a particular residual signal we call generalized innovation is a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions for asymptotically achieving convergence to the true model (consistency), or to the “closest” model according to an information‐theoretic measure (convergence). A simulation example of intention‐aware vehicles at an intersection is given to demonstrate the effectiveness of our approach. © 2020 John Wiley & Sons Ltd.
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.subject
Nonlinear filtering
en_US
dc.subject
State and input estimation
en_US
dc.subject
Switched systems
en_US
dc.subject
Uncertain systems
en_US
dc.title
Simultaneous mode, input and state estimation for switched linear stochastic systems
en_US
dc.type
Journal Article
dc.date.published
2020-11-12
ethz.journal.title
International Journal of Robust and Nonlinear Control
ethz.journal.volume
31
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
Int. j. robust nonlinear control
ethz.pages.start
640
en_US
ethz.pages.end
661
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09574 - Frazzoli, Emilio / Frazzoli, Emilio
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09574 - Frazzoli, Emilio / Frazzoli, Emilio
ethz.date.deposited
2020-11-26T04:36:29Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-08T17:52:17Z
ethz.rosetta.lastUpdated
2021-02-15T23:03:13Z
ethz.rosetta.versionExported
true
ethz.COinS
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