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Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links.

João H Araújo1,2, Joana S Tavares1, Veridiano M Marques1

  • 1INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

A new multiple-lens receiver improves underwater optical wireless communication tolerance to misalignment. This system enhances data reliability for autonomous underwater vehicles (AUVs) and sensor networks.

Keywords:
Monte Carlo simulationmisalignment tolerancemultiple-lens receiveropticalpropagationrandom forestunderwaterwireless

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Area of Science:

  • Optical Wireless Communications
  • Underwater Robotics
  • Photonics

Background:

  • Underwater optical wireless communication (UOWC) links are crucial for autonomous underwater vehicle (AUV) data transmission.
  • Misalignment between AUVs and sensor planes significantly degrades UOWC link performance.
  • Existing systems often lack sufficient tolerance to angular and positional deviations.

Purpose of the Study:

  • To propose and evaluate a multiple-lens receiver scheme for enhancing misalignment tolerance in AUV-to-sensor plane UOWC links.
  • To develop an accurate photon propagation model for UOWC systems considering optical effects and misalignment.
  • To assess the effectiveness of a machine learning algorithm for real-time AUV positioning estimation.

Main Methods:

  • Monte Carlo simulation to model photon propagation, including lens refraction and angular misalignment.
  • Analysis of ideal emitter beam divergence for different transmission lengths (0.5m and 1m).
  • Implementation and testing of a random forest machine learning algorithm for offset and angle estimation.

Main Results:

  • The multiple-lens receiver scheme significantly increases misalignment tolerance compared to a single lens, with a seven-lens system being three times more tolerant.
  • Ideal emitter beam divergence is approximately 15° for 1m and 22° for 0.5m, independent of water turbidity.
  • The random forest algorithm accurately estimated AUV offset and angular misalignment with low mean square errors (e.g., 5mm and 0.157 rad at 1m).

Conclusions:

  • Multiple-lens receiver designs offer a robust solution for improving UOWC link stability in dynamic underwater environments.
  • Optimized emitter beam divergence is critical for maximizing link performance at specific ranges.
  • Machine learning provides a viable method for real-time AUV localization in UOWC systems, enhancing operational efficiency.