Accession Number:

ADA230081

Title:

Classification of Underwater Acoustic Transients by Artificial Neural Networks

Descriptive Note:

Corporate Author:

NAVAL OCEANOGRAPHIC AND ATMOSPHERIC RESEARCH LAB STENNIS SPACE CENTER MS

Personal Author(s):

• Greene, Ronald L.
• Field, Robert L.

Report Date:

1990-01-01

Pagination or Media Count:

2.0

Abstract:

Artificial neural networks have been trained using the backpropagation algorithm to classify a variety of model transient source signals. The networks were then tested on signals propagated to 25 different receiver sites by the time-domain parabolic equation model. Despite the interference effects from surface and bottom reflections, the classification accuracy is about 90 in the noise-free case, virtually identical to that of a nearest-neighbor classifier on the same problem. Classification in the presence of noise is considerably reduced however, the redundancy provided by the multiple receivers in most cases allows the network to correctly classify all signals from sources on which it was trained. In addition, it shows a robustness in the presence of unknown signals not shown by the nearest-neighbor classifier.

Descriptors:

• *MATHEMATICAL MODELS
• TRANSIENTS
• SOURCES
• NEURAL NETS
• MODELS
• SITES
• ACCURACY
• REFLECTION
• SIGNALS
• RECEIVERS
• INTERFERENCE
• CLASSIFICATION
• NOISE REDUCTION
• EQUATIONS
• UNDERWATER ACOUSTICS
• TIME DOMAIN
• REDUNDANCY
• BOTTOM
• PARABOLIC BODIES
• ALGORITHMS

Subject Categories:

• Cybernetics
• Acoustic Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE