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.
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