Hidden Markov Model based Mobility Learning for Improving Indoor Tracking of Mobile Users

Nikolaj Bisgaard Pedersen, Troels Laursen, Jimmy Jessen Nielsen, Tatiana Kozlova Madsen

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8 Citationer (Scopus)

Abstrakt

Indoors, a user's movements are typically confined by walls, corridors, and doorways, and further he is typically repeating the same movements such as walking between certain points in the building. Conventional indoor localization systems do usually not take these properties of the user's movements into account. In this paper we propose a Hidden Markov Model (HMM) based tracking algorithm, which takes a user's previous movements into account. In a quantized grid representation of an indoor scenario, past movement information is used to update the HMM transition probabilities. The user's most likely trajectory is then calculated using and extended version of the Viterbi algorithm. The results show significant improvements of the proposed algorithm compared to a simpler moving average smoothing.
OriginalsprogEngelsk
TitelProceedings of the WPNC 2012
Antal sider5
UdgivelsesstedDresden, Germany
ForlagIEEE Press
Publikationsdato2012
Sider100-104
ISBN (Trykt)978-1-4673-1437-4
DOI
StatusUdgivet - 2012
Begivenhed2012 9th Workshop on Positioning Navigation and Communication - Dresden, Tyskland
Varighed: 15 mar. 201216 mar. 2012

Workshop

Workshop2012 9th Workshop on Positioning Navigation and Communication
LandTyskland
ByDresden
Periode15/03/201216/03/2012

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