AIMC Topic: Ships

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Distinguishing multiple surface ships using one acoustic vector sensor based on a convolutional neural network.

JASA express letters
A direction of arrival (DOA) estimation method based on a convolutional neural network (CNN) using an acoustic vector sensor is proposed to distinguish multiple surface ships in a selected frequency band. The cross-spectrum of the pressure and partic...

Data driven source localization using a library of nearby shipping sources of opportunity.

JASA express letters
A library of broadband (100-1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated o...

Underwater acoustic target recognition using attention-based deep neural network.

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Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but ...

Seabed type and source parameters predictions using ship spectrograms in convolutional neural networks.

The Journal of the Acoustical Society of America
Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on diffe...