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Proceedings Paper

Neural network for passive acoustic discrimination between surface and submarine targets
Author(s): Robert H. Baran; James M. Coughlan
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Paper Abstract

This paper concerns the design, training, test, and evaluation of a feed-forward neural network for classifying acoustic signals emitted by ships in transit. The signals were collected by an omnidirectional hydrophone. Relatively noisy surface ships, moving rapidly at medium to long range, emit signals which superficially resemble those of quieter submarines, moving more slowly and closer to the listening device. The neural network approach is motivated by an obvious analogy to the sonar classifier of Gorman and Sejnowski, who trained a neural network to classify active sonar returns from two undersea objects. The present problem can be solved by a similar network architecture, the outputs indicating which target type (if any) is present. The inputs represent the evolution of spectral densities for each of a number of time lags. Yet the number of target types and encounter geometries is far greater than could possibly be covered in any representative way by a training set comprised of real-world data. Thus, the task is to connect the network to a high fidelity, model-based digital simulator and to show that, by training on the output of the simulator, the neural network can learn to pass realistic tests. This paper describes a neural network design-and-testing exercise based on a simplistic model that captures a few of the salient features of the problem.

Paper Details

Date Published: 1 August 1991
PDF: 13 pages
Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); doi: 10.1117/12.44875
Show Author Affiliations
Robert H. Baran, Naval Surface Warfare Ctr. (United States)
James M. Coughlan, Towson State Univ. (United States)

Published in SPIE Proceedings Vol. 1471:
Automatic Object Recognition
Firooz A. Sadjadi, Editor(s)

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