A neural network approach to multisensor data fusion for vessel traffic services
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Authors
Koh, Leonard Phin-Liong
Subjects
Advisors
Tummala, Murali
Date of Issue
1995-03
Date
March 1995
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
This thesis explores the use of neural networks to perform multisensor data fusion for Vessel Traffic Services (VTS). It begins with a detailed study of the VTS system in order to identify the type of input data and other system features that are suitable for fusion. This is followed by a brief study of the various neural networks to evaluate their suitability for data fusion applications. The Kohonen's self-organizing feature map (SOFM) was identified as the most suitable neural network that can be used for data fusion, but it has some limitations that make it unsuitable for solving the VTS data fusion problem. A neural network data fusion model was proposed that consists of a modified SOFM and a double fusion resolver to solve the problem of double fusion in VTS. The proposed model is simulated in software and tested with measured input data supplied by the U.S. Coast Guard. Results of fusion tests indicate that the proposed fusion system performs well; thus, the proposed neural network fusion model has potential for implementation in the VTS system.
Type
Thesis
Description
Series/Report No
Department
Electrical Engineering
Organization
Identifiers
NPS Report Number
Sponsors
Funder
NA
Format
92 p.
Citation
Distribution Statement
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.