Laplace ℓ1 robust Kalman filter based on majorization minimization

Hongwei Wang, Hongbin Li, Wei Zhang, Heping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an ℓ2 norm format. Furthermore, we implement the MM based robust filter in the Kalman filtering framework and develop a Laplace ℓ1 robust Kalman filter. The proposed algorithm is tested by numerical simulations. The robustness of our algorithm has been borne out when compared with other robust filters, especially in scenarios of heavy outliers.

Original languageEnglish
Title of host publication20th International Conference on Information Fusion, Fusion 2017 - Proceedings
ISBN (Electronic)9780996452700
DOIs
StatePublished - 11 Aug 2017
Event20th International Conference on Information Fusion, Fusion 2017 - Xi'an, China
Duration: 10 Jul 201713 Jul 2017

Publication series

Name20th International Conference on Information Fusion, Fusion 2017 - Proceedings

Conference

Conference20th International Conference on Information Fusion, Fusion 2017
Country/TerritoryChina
CityXi'an
Period10/07/1713/07/17

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