A segmentation-based matching algorithm for magnetic field indoor positioning

Yichen Du, Tughrul Arslan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Magnetic field-based location fingerprinting techniques are emerging technologies used in indoor navigation that take advantage of magnetic field anomalies. k Nearest Neighbours (kNN) is one of the general matching algorithms that is widely used in fingerprint-based indoor positioning systems to estimate the location of users. However, the standard kNN algorithm always visits all the data in a database in order to take the appropriate nearest k neighbours into account while calculating the estimated location. One of the key disadvantages associated with kNN is the fact that computational complexity is quite large. In order to deal with this issue and improve the precision of this method, this paper proposes the use of a new method called Segmentation-based kNN algorithm. This approach conducts suitable selection and partitioning on the target positioning area before calculating the kNN. We have calculated the accuracy rate of the proposed algorithm and compared it with standard kNN algorithm, and the results show that the proposed algorithm performs better than the kNN algorithm with an improvement of 9.24% in average accuracy.

Original languageEnglish
Title of host publication2017 International Conference on Localization and GNSS, ICL-GNSS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538622179
DOIs
Publication statusPublished - 11 Jun 2018
Event2017 International Conference on Localization and GNSS, ICL-GNSS 2017 - Nottingham, United Kingdom
Duration: 27 Jun 201729 Jun 2017

Conference

Conference2017 International Conference on Localization and GNSS, ICL-GNSS 2017
Country/TerritoryUnited Kingdom
CityNottingham
Period27/06/1729/06/17

Keywords

  • k nearest neighbours
  • magnetic field indoor positioning
  • space segmentation

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