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Semi-Supervised Learning for Mobile Robot Localization using Wireless Signal Strengths
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.ORCID iD: 0000-0001-9940-5929
2017 (English)In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a new semi-supervised machine learning for localization. It improves localization efficiency by reducing efforts needed to calibrate labeled training data by using unlabeled data, where training data come from received signal strengths of a wireless communication link. The main idea is to treat training data as spatio-temporal data. We compare the proposed algorithm with the state-of-art semi-supervised learning methods. The algorithms are evaluated for estimating the unknown location of a smartphone mobile robot. The experimental results show that the developed learning algorithm is the most accurate and robust to the varying amount of training data, without sacrificing the computation speed.

Place, publisher, year, edition, pages
2017.
Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220652ISI: 000417415600060ISBN: 978-1-5090-6299-7 OAI: oai:DiVA.org:kth-220652DiVA: diva2:1172930
Conference
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-11Bibliographically approved

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Yoo, JaehyunJohansson, Karl H.

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CiteExportLink to record
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  • apa
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  • ieee
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  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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