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Auxiliary-Particle-Filter-based Two-Filter Smoothing for Wiener State-Space Models
Aalto University.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2018 (English)In: Proceedings of the 21st International Conference on Information Fusion, Cambridge, UK, July, 2018., 2018, p. 1904-1911Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose an auxiliary-particlefilter-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.

Place, publisher, year, edition, pages
2018. p. 1904-1911
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-368624DOI: 10.23919/ICIF.2018.8455323ISI: 000495071900261ISBN: 978-0-9964527-7-9 (print)OAI: oai:DiVA.org:uu-368624DiVA, id: diva2:1268454
Conference
21th International Conference on Information Fusion, 10-13 July 2018, Cambridge, UK
Funder
Swedish Foundation for Strategic Research , RIT15-0012Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-12-10Bibliographically approved

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Schön, Thomas B.

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