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Motion Model for Positioning with Graph-Based Indoor Map

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

Details

Original languageEnglish
Title of host publication2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 27-30 Oct. 2014, Busan, South Korea
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages646-655
Number of pages10
ISBN (Electronic)978-1-4673-8054-6
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventInternational Conference on Indoor Positioning and Indoor Navigation -
Duration: 1 Jan 1900 → …

Conference

ConferenceInternational Conference on Indoor Positioning and Indoor Navigation
Period1/01/00 → …

Abstract

This article presents a training-free probabilistic pedestrian motion model that uses indoor map information represented as a set of links that are connected by nodes. This kind of structure can be modelled as a graph. In the proposed model, as a position estimate reaches a link end, the choice probabilities of the next link are proportional to the total link lengths (TLL), the total lengths of the subgraphs accessible by choosing the considered link alternative. The TLLs can be computed off-line using only the graph, and they can be updated if training data are available. A particle filter in which all the particles move on the links following the TLL-based motion model is formulated. The TLL-based motion model has advantageous theoretical properties compared to the conventional models. Furthermore, the real-data WLAN positioning tests show that the positioning accuracy of the algorithm is similar or in many cases better than that of the conventional algorithms. The TLL-based model is found to be advantageous especially if position measurements are used infrequently, with 10-second or more time intervals.

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