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Ship localization in Santa Barbara Channel using machine learning classifiers.

Haiqiang Niu¹, Emma Ozanich¹, Peter Gerstoft¹

¹Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA hniu@ucsd.edu, ecreeves@ucsd.edu, pgerstoft@ucsd.edu.

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Summary

This summary is machine-generated.

Machine learning classifiers outperform traditional methods for underwater ship range estimation. These advanced techniques accurately locate vessels up to 10 km, even with limited ocean data.

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Area of Science:

Ocean acoustics
Machine learning
Signal processing

Background:

Accurate underwater ship detection is crucial for maritime surveillance and research.
Traditional methods like matched field processing struggle with limited environmental data.
Machine learning offers a potential alternative for improved acoustic-based localization.

Purpose of the Study:

To compare the performance of machine learning classifiers against conventional matched field processing for ocean acoustic-based ship range estimation.
To assess the feasibility of using machine learning for locating unseen acoustic sources in deep water environments.

Main Methods:

Utilized recordings from three different ships of opportunity on a vertical array.
Trained and tested feed-forward neural network and support vector machine classifiers.
Evaluated performance in a deep water (600m) setting within the Santa Barbara Channel Experiment.

Main Results:

Machine learning classifiers significantly outperformed conventional matched field processing.
Classifiers achieved accurate range estimations up to 10 km.
Conventional methods failed at approximately 4 km without accurate environmental information.

Conclusions:

Machine learning classifiers are effective for underwater ship range estimation, especially with limited environmental data.
The study demonstrates the feasibility of employing machine learning for locating unknown acoustic sources.
Advanced machine learning techniques offer superior performance compared to traditional methods in challenging acoustic environments.