Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Area of Science:

• Marine Technology
• Signal Processing
• Artificial Intelligence

Background:

• Current underwater localization systems often depend on anchor nodes or explicit channel modeling.
• Deep Neural Networks (DNNs) show promise for addressing underwater localization challenges.
• Understanding DNN performance under varying conditions is crucial for practical application.

Purpose of the Study:

• To investigate the impact of channel variability, noise, network size, and signal properties on DNN-based underwater localization precision.
• To determine the conditions under which DNNs are most effective for acoustic signal localization in underwater environments.

Main Methods:

• Simulations were conducted to analyze DNN localization accuracy.
• Key parameters studied included channel variability, receiver noise levels, neural network architecture (number of neurons), and acoustic signal characteristics (power, covariance).

Main Results:

• Localization precision using DNNs is sensitive to channel variability and noise levels.
• The number of neurons in the DNN and the utilization of signal power/covariance also affect performance.
• DNNs demonstrate effectiveness in scenarios with low underwater channel variability.

Conclusions:

• Deep Neural Networks present a promising approach for underwater localization, particularly in stable channel conditions.
• Further research is warranted to optimize DNNs for diverse and challenging underwater acoustic environments.
• This study highlights the potential of AI-driven methods for advancing underwater navigation and positioning.