Passive sonar target recognition using a back-propagating neural network.
Authors
Moore, David Franklin
Subjects
Neural Networks
Back-propagation
Passive sonar target recognition
Sonar target modeling
Back-propagation
Passive sonar target recognition
Sonar target modeling
Advisors
Tummala, Murali
Date of Issue
1991-06
Date
1991 Jun
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
The prompt and accurate processing of sonar data is essential in undersea warfare. The ability to quickly detect and classify sonar targets is crucial to the performance and survivability of all navy surface ships and submarines. With the advent of neural network technology, new opportunities have arisen which could greatly enhance current sonar target recognition capabilities. The main objective of this research is to demonstrate the practical usage of neural networks in recognizing the acoustic signatures of passive sonar targets using simulated-at-sea conditions. We will review the theory behind neural networks, the problems associated with recognizing acoustic signals in an underwater environment, and we will make a detailed case study of a neural network's performance using test data generated from simulated sonar targets.
Type
Thesis
Description
Series/Report No
Department
Electrical Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
71 p.;28 cm.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.