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Received Signal Strength-Based Indoor Localization Using Hierarchical Classification.

Chenbin Zhang1, Ningning Qin1, Yanbo Xue2

  • 1Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China.

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|February 21, 2020
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Summary
This summary is machine-generated.

This study introduces a new Wi-Fi Received Signal Strength (RSS)-based indoor localization method. The hierarchical classification approach improves accuracy and reduces positioning errors in real-world environments.

Keywords:
fingerprint positioninghierarchical classificationindoor localizationreceived signal strength

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics

Background:

  • Indoor localization is crucial for various applications, with increasing commercial interest.
  • Wi-Fi Received Signal Strength (RSS)-based methods are attractive due to widespread Wi-Fi access point (AP) deployment and no extra hardware costs.

Purpose of the Study:

  • To propose a novel hierarchical classification-based method for Wi-Fi RSS-based indoor localization.
  • To enhance the generalization capability and accuracy of indoor positioning systems.

Main Methods:

  • Utilized an improved K-Means clustering algorithm to partition the area into overlapping zones.
  • Employed K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) with a one-versus-one strategy for localization.

Main Results:

  • The proposed method demonstrated improved position classification accuracy by 1.4% to 3.2%.
  • Achieved a reduction in average positioning error by 10% to 22% compared to benchmark methods.
  • Validated performance through real-world environment implementation on a tablet.

Conclusions:

  • The hierarchical classification approach offers a significant improvement over existing Wi-Fi RSS-based indoor localization techniques.
  • The method provides a reliable and accurate solution for indoor positioning challenges.