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Robust auxiliary particle filters using multiple importance sampling
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2014 (English)In: Proc. 18th Workshop on Statistical Signal Processing, Piscataway, NJ: IEEE , 2014, 268-271 p.Conference paper, Published paper (Refereed)
Abstract [en]

A poor choice of importance density can have detrimental effect on the efficiency of a particle filter. While a specific choice of proposal distribution might be close to optimal for certain models, it might fail miserably for other models, possibly even leading to infinite variance. In this paper we show how mixture sampling techniques can be used to derive robust and efficient particle filters, that in general performs on par with, or better than, the best of the standard importance densities. We derive several variants of the auxiliary particle filter using both random and deterministic mixture sampling via multiple importance sampling. The resulting robust particle filters are easy to implement and require little parameter tuning.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE , 2014. 268-271 p.
Keyword [en]
Sequential Monte Carlo, particle filter, mixture sampling, multiple importance sampling
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-265972DOI: 10.1109/SSP.2014.6884627ISI: 000361019700068ISBN: 978-1-4799-4975-5 (print)OAI: oai:DiVA.org:uu-265972DiVA: diva2:867139
Conference
SSP 2014, June 29–July 2, Gold Coast, Australia
Available from: 2015-11-04 Created: 2015-11-04 Last updated: 2015-11-04Bibliographically approved

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Schön, Thomas B.

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