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Improving Wi-Fi based Indoor Positioning using Particle Filter based on Signal Strength
Luleå University of Technology.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-4549-6751
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2014 (English)In: IEEE ISSNIP 2014: 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, Piscataway, NJ: IEEE Communications Society, 2014, 6827597Conference paper, Published paper (Refereed)
Abstract [en]

Indoor positioning is recognized as one of the upcoming major applications which can be used in wide variety of applications such as indoor navigation and enterprise asset tracking. The significance of localization in indoor environments have made the use of Wi-Fi based indoor positioning so that it can utilize available current wireless infrastructure and perform positioning very easily. In this paper we introduced a user friendly prototype for Wi-Fi based indoor positioning system where a user can identify its own position in indoor. Wi-Fi received signal strength (RSS) fluctuations over time introduce incorrect positioning. To minimize the fluctuation of RSS, we developed Particle Filters with the prototype. A comparison between with and without Particle Filter for error performance is presented and at the same time it is also noticed that variation in number of particles could change the positioning accuracy. Moreover comparison between calibration data in all directions and in one direction while constructing a radio map is presented.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2014. 6827597
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-40295DOI: 10.1109/ISSNIP.2014.6827597Local ID: f5f53357-c135-4897-8c2d-aeaa5ba3e8dbOAI: oai:DiVA.org:ltu-40295DiVA: diva2:1013817
Conference
International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) : Symposium on Computational Intelligence 21/04/2014 - 24/04/2014
Note
Godkänd; 2014; 20140129 (karand)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-06-29Bibliographically approved

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CiteExportLink to record
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Citation style
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