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A deep learning approach to position estimation from channel impulse responses

Sensors (Switzerland), ISSN: 1424-8220, Vol: 19, Issue: 5
2019
  • 51
    Citations
  • 0
    Usage
  • 63
    Captures
  • 1
    Mentions
  • 22
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    51
  • Captures
    63
  • Mentions
    1
    • Blog Mentions
      1
      • Blog
        1
  • Social Media
    22
    • Shares, Likes & Comments
      22
      • Facebook
        22

Article Description

Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.

Bibliographic Details

Niitsoo, Arne; Edelhäußer, Thorsten; Eberlein, Ernst; Hadaschik, Niels; Mutschler, Christopher

MDPI AG

Chemistry; Computer Science; Physics and Astronomy; Biochemistry, Genetics and Molecular Biology; Engineering

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