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Summary
This summary is machine-generated.

This study introduces a novel method to improve radio-based positioning accuracy in dense multipath (DM) environments. By clustering devices and fusing wideband (WB) and received signal strength (RSS) measurements, it significantly reduces positioning errors.

Keywords:
AoACRLBInternet of ThingsRSSToAindoor positioningsensor fusionwideband

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Area of Science:

  • Wireless communication
  • Localization and positioning systems
  • Signal processing

Background:

  • Dense multipath (DM) channels significantly degrade radio-based positioning accuracy.
  • DM interference affects both time of flight (ToF) and received signal strength (RSS) measurements.
  • Existing methods struggle with accuracy in DM-rich environments.

Purpose of the Study:

  • To propose a robust position estimation approach combining wideband (WB) and RSS measurements.
  • To enhance positioning accuracy in dense multipath (DM) channels.
  • To develop a joint processing method for densely-spaced devices.

Main Methods:

  • Utilizing RSS measurements to form device clusters.
  • Jointly processing WB measurements within clusters to suppress DM influence.
  • Formulating an algorithmic approach for information fusion and deriving the Cramér-Rao lower bound (CRLB).

Main Results:

  • The proposed clustering approach effectively suppresses DM interference.
  • Fusion of WB and RSS measurements leads to robust position estimation.
  • Simulations and real-world data validate the approach, showing significant error reduction.

Conclusions:

  • The developed clustering and data fusion technique significantly improves positioning accuracy in DM channels.
  • The method halves the root-mean-square error (RMSE) from approximately 2m to below 1m.
  • This approach offers a practical solution for accurate localization in challenging radio environments.