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Comprehensive survey of similarity measures for ranked based location fingerprinting algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationIndoor Positioning and Indoor Navigation (IPIN), 2017 International Conference on
Number of pages4
ISBN (Electronic)978-1-5090-6299-7
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventInternational Conference on Indoor Positioning and Indoor Navigation -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Electronic)2471-917X


ConferenceInternational Conference on Indoor Positioning and Indoor Navigation
Period1/01/00 → …


Ranked Based Fingerprinting uses only ordering indices instead of actual Wi-Fi RSS values in order to make the algorithm insensitive to devices. A key component of the RBF algorithm is a similarity measure which is used to compare and find the closest ranked fingerprints. Previous papers study a few similarity measures; here we study 49 similarity measures in a test with a benchmark with publicly available indoor positioning database. For different similarity measures the positioning accuracy varies from 15.80 m to 55.22 m. The top 3 similarity measures are Lorentzian, Hamming and Jaccard. Hamming and Jaccard similarity measures have been studied in other papers while Lorenzian had not been studied with that kind of problems.

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