A comparison of iteratively reweighted least squares and Kalman Filter with em in measurement error covariance estimation

Yanbo Yang, Tim Brown, Bill Moran, Xuezhi Wang, Quan Pan, Yuemei Qin

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

2 Scopus citations

Abstract

An unknown measurement error covariance in a stochastic dynamical system is to be estimated from measurements. A least squares approach is implemented by extending the iteratively reweighted least squares (IRLS) technique to handle system dynamics over a time window. The performance of this method, in terms of convergence rate and error, is compared to the standard Kalman Filter Expectation-Maximization (KFEM) approach via simulations of a single moving target with known stochastic dynamics tracked by two sensor measurements. We demonstrate that the extended IRLS outperforms KFEM in estimation accuracy. It also has a slightly better convergence rate at most epochs under any of a more uncertain, less uncertain, or re-estimated prior for the KFEM method.

Original languageEnglish
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages286-291
Number of pages6
ISBN (Electronic)9780996452748
StatePublished - 1 Aug 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: 5 Jul 20168 Jul 2016

Publication series

NameFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

Conference

Conference19th International Conference on Information Fusion, FUSION 2016
Country/TerritoryGermany
CityHeidelberg
Period5/07/168/07/16

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