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Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning.

Joan Bordoy1, Dominik Jan Schott2, Jizhou Xie1

  • 1Department of Computer Science (IIF), University of Freiburg, 79110 Freiburg, Germany.

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Summary

This study introduces two methods to locate acoustic transmitters without manually measuring microphone positions. One uses an inertial measurement unit (IMU) for receiver positioning, while the other employs machine learning for TDoA signature analysis.

Keywords:
indoor localizationlocalizationmachine learningrandom forestself-calibrationtdoaultrasound

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Area of Science:

  • Acoustic signal processing
  • Sensor fusion
  • Machine learning for localization

Background:

  • Acoustic transmitter localization typically relies on synchronized microphones measuring time differences of arrival (TDoA).
  • Accurate localization requires precise, pre-measured microphone positions, which is labor-intensive and error-prone.
  • Existing methods struggle with unknown receiver positions and non-line-of-sight conditions.

Purpose of the Study:

  • To develop novel methods for acoustic transmitter localization that eliminate the need for manual microphone position measurement.
  • To improve the robustness and accuracy of acoustic localization systems.
  • To explore alternative data sources and machine learning techniques for enhanced positioning.

Main Methods:

  • Method 1: Incorporating data from an inertial measurement unit (IMU) alongside acoustic TDoA measurements to estimate receiver positions.
  • Method 2: Utilizing machine learning, specifically artificial neural networks and random forest classification, to learn TDoA signatures of different regions for localization.
  • Both methods aim to overcome the limitations of traditional TDoA localization by removing the dependency on known microphone coordinates.

Main Results:

  • The IMU-assisted method enhances localization accuracy and reduces susceptibility to large measurement errors by aiding non-convex optimizers.
  • Machine learning approaches successfully enable localization without prior knowledge of microphone positions or signal line-of-sight status.
  • Both novel methods demonstrate increased success rates in acoustic transmitter localization compared to traditional techniques.

Conclusions:

  • Manual measurement of microphone positions is a significant bottleneck in acoustic localization systems.
  • Integrating IMUs or employing machine learning-based TDoA signature analysis offers robust and accurate alternatives for acoustic transmitter localization.
  • These advancements pave the way for more practical and efficient acoustic positioning solutions in various applications.