Accession Number : ADA294251

Title :   A Neural Network Approach to Multisensor Data Fusion for Vessel Traffic Services.

Descriptive Note : Master's thesis,

Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s) : Koh, Leonard P.

PDF Url : ADA294251

Report Date : MAR 1995

Pagination or Media Count : 104

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.

Descriptors :   *NEURAL NETS, *DATA FUSION, *MULTISENSORS, *COAST GUARD OPERATIONS, *VESSEL TRAFFIC SYSTEMS, COMPUTER PROGRAMS, ALGORITHMS, COMPUTERIZED SIMULATION, INPUT, DISTRIBUTED DATA PROCESSING, PARAMETERS, THESES, PARALLEL PROCESSING, PROBLEM SOLVING, MAPS, NUCLEAR FUSION, SELF ORGANIZING SYSTEMS.

Subject Categories : Electrical and Electronic Equipment
      Marine Engineering

Distribution Statement : APPROVED FOR PUBLIC RELEASE