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AI-Driven Decimeter-Level Indoor Localization Using Single-Link Wi-Fi: Adaptive Clustering and Probabilistic

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Summary
This summary is machine-generated.

This study introduces an AI framework for precise indoor Wi-Fi localization. It achieves decimeter-level accuracy by intelligently handling signal interference and improving time-of-flight and angle-of-arrival estimations.

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Wi-Fi sensingadaptive clusteringchannel state information (CSI)indoor localizationunsupervised learning

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Area of Science:

  • Artificial Intelligence
  • Wireless Communication
  • Indoor Localization

Background:

  • Complex indoor environments present challenges for localization due to multipath interference and signal dynamics.
  • Existing Wi-Fi localization methods struggle with accuracy and robustness in real-world scenarios.
  • Need for adaptive and unsupervised AI techniques for reliable indoor positioning.

Purpose of the Study:

  • To develop an AI-driven framework for high-precision indoor localization using Wi-Fi Channel State Information (CSI).
  • To enhance robustness and accuracy in challenging multipath environments.
  • To enable real-time deployment compatible with commodity hardware.

Main Methods:

  • Integration of adaptive and unsupervised AI modules into the localization pipeline.
  • A refined two-stage time-of-flight (TOF) estimation combining minimum-norm and probability-weighted refinement for non-line-of-sight (NLOS) conditions.
  • Adaptive parameter-tuned DBSCAN for unsupervised spatio-temporal clustering of angle-of-arrival (AOA) sequences.

Main Results:

  • Achieved decimeter-level accuracy with an average localization error of 0.63 m on the Widar2.0 dataset.
  • Demonstrated superior performance over existing methods like Widar2.0 and Dynamic-MUSIC in accuracy and efficiency.
  • Successfully suppressed multipath interference and eliminated positioning ambiguity.

Conclusions:

  • The proposed AI framework offers a robust and accurate solution for indoor localization using Wi-Fi CSI.
  • The intelligent and lightweight architecture is suitable for real-time applications on commodity hardware.
  • Significant potential for applications such as human tracking, smart building navigation, and location-aware AI.