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Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

Wen Liu1, Xiao Fu2, Zhongliang Deng3

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China. liuwen@bupt.edu.cn.

Sensors (Basel, Switzerland)
|December 6, 2016
PubMed
Summary

This study introduces a new Wi-Fi fingerprinting method, Smallest-Enclosing-Circle-based (SEC) clustering, to improve indoor location accuracy. The SEC algorithm significantly enhances positioning precision in complex indoor environments.

Keywords:
K-meansaccess point deploymentclustering algorithmcoordinate based clusteringfingerprintingindoor positioningsmallest enclosing circle

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics

Background:

  • Indoor positioning is crucial for location-based services due to the lack of Global Navigation Satellite System (GNSS) signals indoors.
  • Wi-Fi fingerprinting is a popular indoor positioning technique due to its widespread infrastructure, flexibility, and robustness.
  • Access Point (AP) deployment significantly impacts the accuracy of Wi-Fi fingerprinting systems.

Purpose of the Study:

  • To analyze the influence of Access Point (AP) deployment on Wireless Local Area Network (WLAN) based indoor fingerprinting location.
  • To investigate the benefits of coordinate-based clustering over traditional Received Signal Strength (RSS)-based clustering for indoor positioning.
  • To propose a novel coordinate-based clustering method, Smallest-Enclosing-Circle-based (SEC), to mitigate positioning errors caused by AP deployment and enhance robustness in cluttered environments.

Main Methods:

  • Analysis of AP deployment effects on WLAN fingerprinting accuracy.
  • Comparison of coordinate-based clustering with traditional RSS-based clustering (e.g., K-means).
  • Development and implementation of the Smallest-Enclosing-Circle-based (SEC) clustering algorithm for indoor fingerprinting.

Main Results:

  • The SEC clustering algorithm demonstrates superior performance compared to traditional RSS-based clustering.
  • Significant improvements in positioning accuracy were observed: 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1, and 73.7% for Test-bed 2 Floor 2.
  • The SEC method effectively reduces positioning errors related to AP deployment and enhances robustness in dense, cluttered indoor settings.

Conclusions:

  • The proposed SEC clustering algorithm offers a significant advancement in Wi-Fi fingerprinting for indoor positioning.
  • Coordinate-based clustering, specifically the SEC method, provides a more accurate and robust solution than RSS-based approaches.
  • This research contributes to the development of more reliable indoor location-based services (ILBS).