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Low-dimensional signal-strength fingerprint-based positioning in wireless LANs
KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
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2014 (English)In: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 12, no 1, 100-114 p.Article in journal (Refereed) Published
Abstract [en]

Accurate location awareness is of paramount importance in most ubiquitous and pervasive computing applications. Numerous solutions for indoor localization based on IEEE802.11, bluetooth, ultrasonic and vision technologies have been proposed. This paper introduces a suite of novel indoor positioning techniques utilizing signal-strength (SS) fingerprints collected from access points (APs). Our first approach employs a statistical representation of the received SS measurements by means of a multivariate Gaussian model by considering a discretized grid-like form of the indoor environment and by computing probability distribution signatures at each cell of the grid. At run time, the system compares the signature at the unknown position with the signature of each cell by using the Kullback-Leibler Divergence (KLD) between their corresponding probability densities. Our second approach applies compressive sensing (CS) to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server. The performance evaluation which was conducted at the premises of a research laboratory and an aquarium under real-life conditions, reveals that the proposed statistical fingerprinting and CS-based localization techniques achieve a substantial localization accuracy.

Place, publisher, year, edition, pages
2014. Vol. 12, no 1, 100-114 p.
Keyword [en]
Compressive sensing, Sparse representation, Multivariate Gaussian model, Kullback-Leibler divergence, RSSI measurements, Localization
National Category
Telecommunications Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-141299DOI: 10.1016/j.adhoc.2011.12.006ISI: 000329957300009Scopus ID: 2-s2.0-84888641984OAI: oai:DiVA.org:kth-141299DiVA: diva2:696281
Funder
EU, FP7, Seventh Framework Programme, PIAP-GA-2009-251605
Note

QC 20140213

Available from: 2014-02-13 Created: 2014-02-13 Last updated: 2017-12-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf