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Research on a Fast Matching Method of K Nearest Neighbor for WiFi Fingerprint Location

  • Lijun Hou
  • Yufeng Luo
  • Yanhui Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

Aiming at the problem of low speed and positioning fluctuations of indoor WiFi fingerprints. Firstly, we use the method of Gauss fitting and averaging to acquire the average value of the received signal. Secondly, we use a distance to be similarity measure to define a threshold to classify the fingerprint database. Finally, By improving the K nearest neighbor algorithm and on the basis of classification, Implement fast matching of K nearest neighbor. The experimental results show that the time efficiency of the classified location system has been greatly improved, with an average decrease of 62.8%; In the positioning accuracy, WiFi fingerprint positioning of the average error from 4.17 m down to 2.12 m.

Keywords

Gaussian fitting Multiple measurements for averaging Database classification Fast matching of K nearest neighbor 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering & AutomationHenan Polytechnic UniversityJiaozuoChina
  2. 2.Wuxi Jiuyu Architectural Design Institute Co. Ltd.WuxiChina

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