Experimental Analysis on Weight K-Nearest Neighbor Indoor Fingerprint Positioning

Jiusong Hu, Dawei Liu*, Zhi Yan, Hongli Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)

Abstract

Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted K -nearest neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive WKNN (SAWKNN) algorithm with a dynamic K}. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K = 1. The performance of the proposed algorithm has been evaluated in real-world experiments.

Original languageEnglish
Article number8430378
Pages (from-to)891-897
Number of pages7
JournalIEEE Internet of Things Journal
Volume6
Issue number1
DOIs
Publication statusPublished - Feb 2019

Keywords

  • Fingerprint
  • WiFi
  • indoor location
  • weighted K-nearest neighbor (WKNN)

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