Expectation maximisation for sensor data fusion
| dc.contributor.author | Opitz, Felix | |
| dc.contributor.editor | Hochberger, Christian | |
| dc.contributor.editor | Liskowsky, Rüdiger | |
| dc.date.accessioned | 2019-06-12T12:32:20Z | |
| dc.date.available | 2019-06-12T12:32:20Z | |
| dc.date.issued | 2006 | |
| dc.description.abstract | 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. | en |
| dc.identifier.isbn | 978-3-88579-187-4 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/23693 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2006 – Informatik für Menschen, Band 1 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-93 | |
| dc.title | Expectation maximisation for sensor data fusion | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 322 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 318 | |
| gi.conference.date | 2.-6. Oktober 2006 | |
| gi.conference.location | Dresden | |
| gi.conference.sessiontitle | Regular Research Papers |
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