Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hostettler, Roland
dc.contributor.author Schön, Thomas B.
dc.date.accessioned 2018-12-10T10:32:42Z
dc.date.available 2018-12-10T10:32:42Z
dc.date.issued 2018-09-05
dc.identifier.citation Hostettler , R & Schön , T B 2018 , Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models . in Proceedings of the 21st International Conference on Information Fusion, FUSION 2018 . , 8455323 , Institute of Electrical and Electronics Engineers , pp. 1904-1911 , International Conference on Information Fusion , Cambridge , United Kingdom , 10/07/2018 . DOI: 10.23919/ICIF.2018.8455323 en
dc.identifier.isbn 9780996452762
dc.identifier.other PURE UUID: e538b47e-cbfa-44d9-9e34-9bc753472a44
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/auxiliaryparticlefilterbased-twofilter-smoothing-for-wiener-statespace-models(e538b47e-cbfa-44d9-9e34-9bc753472a44).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85054083808&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/29617926/2018_fusion_ps.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35315
dc.description.abstract In this paper, we propose an auxiliary-particle-filter-based two-filter smoother for Wiener state-space models. The proposed smoother exploits the model structure in order to obtain an analytical solution for the backward dynamics, which is introduced artificially in other two-filter smoothers. Furthermore, Gaussian approximations to the optimal proposal density and the adjustment multipliers are derived for both the forward and backward filters. The proposed algorithm is evaluated and compared to existing smoothing algorithms in a numerical example where it is shown that it performs similarly to the state of the art in terms of the root mean squared error at lower computational cost for large numbers of particles. en
dc.format.extent 8
dc.format.extent 1904-1911
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof International Conference on Information Fusion en
dc.relation.ispartofseries Proceedings of the 21st International Conference on Information Fusion, FUSION 2018 en
dc.rights openAccess en
dc.subject.other Computer Vision and Pattern Recognition en
dc.subject.other Signal Processing en
dc.subject.other Statistics, Probability and Uncertainty en
dc.subject.other Instrumentation en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.subject.other 113 Computer and information sciences en
dc.title Auxiliary-Particle-Filter-Based Two-Filter Smoothing for Wiener State-Space Models en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.contributor.department Uppsala University
dc.subject.keyword particle filtering
dc.subject.keyword Sequential Monte Carlo
dc.subject.keyword state estimation
dc.subject.keyword state-space methods
dc.subject.keyword state-space models
dc.subject.keyword Wiener models
dc.subject.keyword Computer Vision and Pattern Recognition
dc.subject.keyword Signal Processing
dc.subject.keyword Statistics, Probability and Uncertainty
dc.subject.keyword Instrumentation
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812106330
dc.identifier.doi 10.23919/ICIF.2018.8455323
dc.type.version acceptedVersion


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