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Accurate Indoor Localization Based on CSI and Visibility Graph.

Zhefu Wu1, Lei Jiang2, Zhuangzhuang Jiang3

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. wuzf@zjut.edu.cn.

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Summary
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This study introduces a new passive indoor localization method using channel state information (CSI). The technique enhances accuracy by combining statistical and network features, achieving 96% precision for reliable indoor positioning.

Keywords:
CSIindoor localizationvisibility graph

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Passive indoor localization offers nonintrusive applications in security, logistics, and healthcare.
  • Existing nonintrusive techniques suffer from inaccuracies due to unpredictable indoor environment dynamics.

Purpose of the Study:

  • To develop a highly accurate passive indoor localization technique using channel state information (CSI).
  • To address the limitations of current nonintrusive localization methods.

Main Methods:

  • Developed a novel passive indoor localization algorithm integrating intra-subcarrier statistics and inter-subcarrier network features.
  • Modified the visibility graph (VG) technique to construct complex networks tailored for indoor localization.
  • Validated the technique through real-world deployments.

Main Results:

  • The proposed CSI-based technique achieved an average accuracy of approximately 96%.
  • Demonstrated a performance improvement of over 9% compared to existing state-of-the-art methods.
  • Effectiveness confirmed via practical, real-world experimental deployments.

Conclusions:

  • The novel algorithm effectively combines statistical and network features for improved passive indoor localization.
  • The modified visibility graph approach provides a robust network representation for localization.
  • The CSI-based technique offers a significant advancement in accurate and reliable nonintrusive indoor positioning.