Expectation maximisation for sensor data fusion

Zusammenfassung
The expectation maximisation algorithm offers several applications in sensor data fusion. An overview of some of this applications and a short course in expectation maximisation algorithm and its properties is given. The expectation maximisation algorithm (EM) was introduced by Dempster, Laird and Rubin in 1977 [DLR77]. The basic of expextation maximisation is maximum likelihood estimation (MLE). In modern sensor data fusion expectation maximisation becomes a substantial part in several applications, e.g. multi target tracking with probabilistic multi hypothesis tracking (PMHT), target extraction within probability hypothesis density (PHD) filter, cluster analysis within multidimensional data association, or image computing.
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  • BibTeX
Opitz, F., (2006). Expectation maximisation for sensor data fusion. In: Hochberger, C. & Liskowsky, R. (Hrsg.), INFORMATIK 2006 – Informatik für Menschen, Band 1. Bonn: Gesellschaft für Informatik e.V.. (S. 318-322).
@inproceedings{mci/Opitz2006,
author = {Opitz, Felix},
title = {Expectation maximisation for sensor data fusion},
booktitle = {INFORMATIK 2006 – Informatik für Menschen, Band 1},
year = {2006},
editor = {Hochberger, Christian AND Liskowsky, Rüdiger} ,
pages = { 318-322 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
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Mehr Information

ISBN: 978-3-88579-187-4
ISSN: 1617-5468
Datum: 2006
Sprache: en (en)
Typ: Text/Conference Paper
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