Toward A Dynamic K in K-nearest neighbor fingerprint indoor positioning

Jiusong Hu, Hongli Liu, Dawei Liu

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

3 Citations (Scopus)

Abstract

K-nearest neighbor (KNN) fingerprint positioning is a promising solution for WLAN-based indoor positioning that has received much attention over the past ten years. In order to achieve good positioning results, much effort has been made to develop advanced KNN algorithms and to find the optimal K. So far most of the work has concentrated on using a fixed K for a given positioning system. A drawback is that the positioning system would become unstable since the best K at one place may not be the best for another. In order to address this problem, we propose a dynamic KNN algorithm that can adjust the value of K dynamically to offset noises of different levels. The value adjustment is made based on the WiFi signals measured in realtime, therefore, it does not require any prior knowledge of the WLAN or the indoor environment. Analysis on field measurement data shows that, for a large percentage of the positioning area, the best K is 1 instead of 3 or 5 found in previous studies. The field experiment also shows, by setting K=1, the proposed method can achieve better positioning accuracy compared with the classical KNN method.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages308-314
Number of pages7
ISBN (Print)9781538626597
DOIs
Publication statusPublished - 2 Aug 2018
Event19th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2018 - Salt Lake City, United States
Duration: 7 Jul 20189 Jul 2018

Publication series

NameProceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018

Conference

Conference19th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2018
Country/TerritoryUnited States
CitySalt Lake City
Period7/07/189/07/18

Keywords

  • Fingerprint positioning
  • Indoor positioning
  • K-nearest neighbor

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