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Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments.

Gianluca Gennarelli1, Obada Al Khatib2, Francesco Soldovieri3

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This study enhances indoor positioning by using radio-frequency (RF) source localization with sensor arrays. It improves accuracy in various conditions for better location-based services.

Keywords:
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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Indoor positioning is crucial for applications like tracking, navigation, and emergency detection.
  • Despite advancements, accurate indoor positioning remains a research challenge, especially with wireless technologies.
  • Active radio-frequency (RF) source localization offers a promising approach for precise indoor location determination.

Purpose of the Study:

  • To investigate the effectiveness of physical layer RF source localization using sensor arrays for indoor environments.
  • To analyze the impact of different measurement configurations and data processing strategies on localization accuracy.
  • To evaluate performance under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions.

Main Methods:

  • Formulating the indoor localization problem as an imaging task using the inverse source approach.
  • Deploying receiving sensor arrays at the surveillance region's border to capture signals from the active RF source.
  • Examining various data processing and fusion strategies to enhance localization precision.

Main Results:

  • Demonstrated the feasibility of RF source localization at the physical layer for indoor environments.
  • Quantified the impact of different sensor configurations and data processing techniques on positioning accuracy.
  • Showcased performance under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios.

Conclusions:

  • Physical layer RF source localization with sensor arrays is effective for indoor positioning.
  • The choice of measurement configuration and data processing significantly influences localization accuracy.
  • The inverse source approach provides a robust framework for addressing indoor RF localization challenges.