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dc.contributor.author
Surace, Simone C.
dc.contributor.author
Kutschireiter, Anna
dc.contributor.author
Pfister, Jean-Pascal
dc.date.accessioned
2020-01-21T09:25:18Z
dc.date.available
2020-01-19T22:31:59Z
dc.date.available
2020-01-21T09:25:18Z
dc.date.issued
2020-04
dc.identifier.issn
2475-1456
dc.identifier.other
10.1109/LCSYS.2019.2951093
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/391812
dc.identifier.doi
10.3929/ethz-b-000391812
dc.description.abstract
The filtering of a Markov diffusion process on a manifold from counting process observations leads to ‘large’ changes in the conditional distribution upon an observed event, corresponding to a multiplication of the density by the intensity function of the observation process. If that distribution is represented by unweighted samples or particles, they need to be jointly transformed such that they sample from the modified distribution. In previous work, this transformation has been approximated by a translation of all the particles by a common vector. However, such an operation is ill-defined on a manifold, and on a vector space, a constant gain can lead to a wrong estimate of the uncertainty over the hidden state. Here, taking inspiration from the feedback particle filter (FPF), we derive an asymptotically exact filter (called ppFPF) for point process observations, whose particles evolve according to intrinsic (i.e., parametrization-invariant) dynamics that are composed of the dynamics of the hidden state plus additional control terms. While not sharing the gain-times-error structure of the FPF, the optimal control terms are expressed as solutions to partial differential equations analogous to the weighted Poisson equation for the gain of the FPF. The proposed filter can therefore make use of existing approximation algorithms for solutions of weighted Poisson equations.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Filtering
en_US
dc.subject
estimation
en_US
dc.subject
stochastic systems
en_US
dc.subject
mean field games
en_US
dc.subject
stochastic optimal control
en_US
dc.title
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-11-04
ethz.journal.title
IEEE Control Systems Letters
ethz.journal.volume
4
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
480
en_US
ethz.pages.end
485
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2020-01-19T22:32:21Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-21T09:25:29Z
ethz.rosetta.lastUpdated
2020-02-15T23:43:25Z
ethz.rosetta.versionExported
true
ethz.COinS
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