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Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Deep Learning Indoor Positioning for Connected Aircraft Cabins: A ResNet Approach with Real-World Validation.

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  • 1Transport Systems Information Technology, TUD Dresden University of Technology, 01062 Dresden, Germany.

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Summary

This study introduces a deep learning approach using a residual neural network (ResNet) for precise indoor positioning in aircraft cabins. The ResNet method significantly improved localization accuracy compared to traditional techniques.

Keywords:
Bluetooth Low Energy (BLE)ResNet architectureUltra-Wide Band (UWB)likelihood grid mapsmultilaterationsensor fusion

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Aerospace Engineering

Background:

  • Indoor positioning in aircraft cabins faces challenges like multipath propagation and non-line-of-sight conditions due to metallic fuselage.
  • Existing radio-based positioning methods are degraded by these challenging cabin environments.

Purpose of the Study:

  • To validate a deep learning approach, specifically a residual neural network (ResNet), for accurate aircraft cabin localization.
  • To assess the performance of the ResNet-based method against established positioning algorithms.

Main Methods:

  • Utilized dual-technology ranging from Ultra-Wideband (UWB) and Bluetooth Low Energy (BLE).
  • Transformed range observations into spatial likelihood representations processed by a ResNet.
  • Conducted real-world measurements in an A320 cabin mockup with 19 distributed positions.

Main Results:

  • ResNet achieved a median positioning error of 0.177 m, outperforming iterative least squares, robust least squares, and Bayesian grid filtering.
  • Demonstrated the viability of likelihood-based neural network positioning for aircraft cabin applications.
  • Identified performance dependencies on anchor visibility, measurement height, and propagation conditions.

Conclusions:

  • Deep learning, particularly ResNet, offers a viable solution for precise indoor positioning in challenging aircraft cabin environments.
  • The developed method shows superior accuracy compared to baseline techniques.
  • Further research should consider environmental factors for optimal deployment.