Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Tronarp, Filip
dc.contributor.author Hostettler, Roland
dc.contributor.author Särkkä, Simo
dc.date.accessioned 2017-10-13T10:33:19Z
dc.date.available 2017-10-13T10:33:19Z
dc.date.issued 2016-07-05
dc.identifier.citation Tronarp , F , Hostettler , R & Särkkä , S 2016 , Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise . in Proceedings of the 19th International Conference on Information Fusion, FUSION 2016 . , 7528109 , IEEE , pp. 1859 - 1866 , International Conference on Information Fusion , Heidelberg , Germany , 5-8 July . en
dc.identifier.isbn 978-0-9964527-4-8
dc.identifier.other PURE UUID: 3be542fe-94f8-4cbc-86fa-96ae3f4ea36d
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/sigmapoint-filtering-for-nonlinear-systems-with-nonadditive-heavytailed-noise(3be542fe-94f8-4cbc-86fa-96ae3f4ea36d).html
dc.identifier.other PURE LINK: http://ieeexplore.ieee.org/document/7528109/
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/15093348/2016_fusion.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/28159
dc.description.abstract This paper is concerned with sigma-point methods for filtering in nonlinear systems, where the process and measurement noise are heavy tailed and enter the system nonadditively. The problem is approached within the framework of assumed density filtering and the necessary statistics are approximated using sigma-point methods developed for Student’s t-distribution. This leads to UKF/CKF-type of filters for Student’s t-distribution. Four different sigma-point methods are considered that compute exact expectations of polynomials for orders up to 3, 5, 7, and 9, respectively. The resulting algorithms are evaluated in a simulation example and real data from a pedestrian dead-reckoning experiment. In the simulation experiment the nonlinear Student’s t filters are found to be faster in suppressing large errors in the state estimates in comparison to the UKF when filtering in nonlinear Gaussian systems with outliers in process and measurement noise. In the pedestrian dead-reckoning experiment the sigma-point Student’s t filter was found to yield better loop closure and path length estimates as well as significantly improved robustness towards extreme accelerometer measurement spikes. en
dc.format.extent 1859 - 1866
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 19th International Conference on Information Fusion, FUSION 2016 en
dc.rights openAccess en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.title Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.identifier.urn URN:NBN:fi:aalto-201710137020
dc.type.version acceptedVersion


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