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Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error.

Stefania Monica1, Gianluigi Ferrari2

  • 1Department of Mathematics, Physics and Computer Science, University of Parma, 43124 Parma, Italy. stefania.monica@unipr.it.

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|May 19, 2018
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Summary

This study enhances indoor localization accuracy using Ultra Wide Band (UWB) technology by developing a statistical model for distance errors. Applying this model significantly reduces localization errors in Internet of Things (IoT) applications.

Keywords:
Internet of Thingsexperimental modelindoor localizationleast square methodultra wide band

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Area of Science:

  • * Computer Science
  • * Electrical Engineering
  • * Robotics

Background:

  • * The Internet of Things (IoT) is rapidly expanding, driving demand for precise indoor localization and context awareness.
  • * Ultra Wide Band (UWB) technology offers high accuracy for indoor positioning but is sensitive to distance estimation errors.
  • * Current localization algorithms often rely on estimated inter-node distances, necessitating error modeling.

Purpose of the Study:

  • * To evaluate the performance improvement of indoor localization algorithms by incorporating a statistical model for Ultra Wide Band (UWB) distance errors.
  • * To propose a novel statistical model for range estimation error between UWB nodes.
  • * To demonstrate the effectiveness of the proposed model in realistic indoor scenarios.

Main Methods:

  • * Extensive experimental measurement campaign to collect UWB ranging data.
  • * Development of a general analytical framework based on the Least Squares (LS) method.
  • * Derivation of a linear statistical model for UWB distance error.
  • * Application of the statistical model to enhance existing localization algorithms.

Main Results:

  • * The proposed statistical model effectively characterizes the distance error in UWB systems.
  • * Integration of the statistical model significantly improved the accuracy of tested localization algorithms.
  • * Localization error was reduced by up to 66% in various realistic scenarios.

Conclusions:

  • * A novel statistical model for UWB range estimation error can substantially enhance indoor localization accuracy.
  • * The proposed method provides a practical framework for improving IoT indoor positioning systems.
  • * Accurate error modeling is crucial for realizing the full potential of UWB in indoor localization.