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A Deep Learning Approach to Position Estimation from Channel Impulse Responses.

Arne Niitsoo1, Thorsten Edelhäußer2, Ernst Eberlein3

  • 1Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nürnberg, Germany. arne.niitsoo@iis.fraunhofer.de.

Sensors (Basel, Switzerland)
|March 6, 2019
PubMed
Summary

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This summary is machine-generated.

Deep learning (DL) accurately estimates mobile object positions using raw channel impulse responses (CIR) from radio signals. This advanced method excels in challenging industrial settings with multipath propagation and even surpasses existing techniques in clear line-of-sight scenarios.

Area of Science:

  • Robotics and Automation
  • Signal Processing
  • Machine Learning

Background:

  • Radio-based locating systems are crucial for industrial digitalization, enabling continuous tracking in production, manufacturing, and warehousing.
  • Time difference of arrival (TDoA) systems use synchronized antennas to trilaterate mobile tag positions based on signal time-of-flight (ToF).
  • Multipath propagation in industrial environments significantly hinders accurate ToF extraction, limiting traditional locating system performance.

Purpose of the Study:

  • To investigate the application of deep learning (DL) for direct mobile object position estimation.
  • To evaluate the effectiveness of DL in overcoming multipath propagation challenges in industrial environments.
  • To compare the performance of DL-based positioning against state-of-the-art methods.
Keywords:
channel impulse responseconvolutional neural networksdeep learningdistributed CNNmachine learningposition estimationradio-based real-time locating systemstime difference of arrivaltime of arrival

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Main Methods:

  • Utilizing deep learning (DL) models to process raw channel impulse responses (CIR) from radio signals.
  • Directly estimating mobile object positions from CIR data, bypassing traditional ToF extraction.
  • Conducting experiments in industrial environments characterized by significant multipath propagation.

Main Results:

  • The proposed DL-based position estimation method demonstrates robust performance in harsh multipath environments.
  • DL-based positioning significantly outperforms existing state-of-the-art approaches.
  • The method also shows superior accuracy in line-of-sight (LoS) situations compared to conventional techniques.

Conclusions:

  • Deep learning offers a powerful solution for accurate mobile object localization in challenging industrial settings.
  • DL effectively mitigates the negative impacts of multipath propagation on radio-based locating systems.
  • The DL approach provides a superior alternative to traditional TDoA methods, enhancing industrial process digitalization.