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- Kronander, Joel,1983-Linköpings universitet,Medie- och Informationsteknik,Tekniska högskolan,Datorgrafik och bildbehandling (author)
Robust auxiliary particle filters using multiple importance sampling
- Article/chapterEnglish2014
Publisher, publication year, extent ...
- Piscataway, NJ :IEEE,2014
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- LIBRIS-ID:oai:DiVA.org:uu-265972
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-265972URI
- https://doi.org/10.1109/SSP.2014.6884627DOI
- https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-107134URI
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- Language:English
- Summary in:English
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- 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.
Subject headings and genre
- TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
- Sequential Monte Carlo
- particle filter
- mixture sampling
- multiple importance sampling
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- Schön, Thomas B.Uppsala universitet,Avdelningen för systemteknik,Reglerteknik,Uppsala Universitet, Sweden,Information Technology(Swepub:uu)thosc112 (author)
- Linköpings universitetMedie- och Informationsteknik (creator_code:org_t)
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- In:Proc. 18th Workshop on Statistical Signal ProcessingPiscataway, NJ : IEEE, s. 268-2719781479949755
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