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Mapless Indoor Localization by Trajectory Learning from a Crowd
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2016 (English)In: 2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), Institute of Electrical and Electronics Engineers (IEEE), 2016, article id 7743685Conference paper, Published paper (Refereed)
Abstract [en]

This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it can use the alternative map as the probabilistic function (the prior), and can use probabilistic relationship (the likelihood) between wifi RSSs and location. Field experimental results confirm the usefulness of our algorithm when the map is not available and robustness against outliers, in that the accuracy of the proposed localization is similar to that using the true map information.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. article id 7743685
Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
Keywords [en]
Artificial intelligence, Learning algorithms, Learning systems, Wireless local area networks (WLAN), Dynamic time warping, Gaussian process, Indoor localization, Machine learning techniques, Multiple people, Position estimation, Probabilistic functions, Wifi received signal strengths
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-199802DOI: 10.1109/IPIN.2016.7743685ISI: 000390141300104Scopus ID: 2-s2.0-85004000531ISBN: 978-1-5090-2425-4 (print)OAI: oai:DiVA.org:kth-199802DiVA, id: diva2:1066862
Conference
International Conference on Indoor Positioning and Indoor Navigation (IPIN), OCT 04-07, 2016, Madrid, SPAIN
Note

QC 20170119

Available from: 2017-01-19 Created: 2017-01-16 Last updated: 2017-01-19Bibliographically approved

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Yoo, JaehyunJohansson, Karl H.

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