By Michael Bahner, Jürgen Dick, Bernd Kardatzki, Hanns Ruder (auth.), S. Pfleger, J. Gonçalves, D. Vernon (eds.)
Data fusion, the power to mix information derived from numerous assets to supply a coherent, informative, and beneficial characterization of a situation,is a demanding activity. there is not any unified and confirmed answer that's acceptable in all situations, yet there are numerous believable and priceless methods that are and are used to resolve specific purposes. This quantity offers the complaints of the workshop information Fusion functions hosted in Brussels via the 1992 ESPRIT convention and Exhibition. It includes 22 papers from sixty nine experts,who current complex study effects on info fusion including practicalsolutions to multisensor facts fusion in a large choice of purposes: real-time specialist structures, robotics, clinical prognosis and sufferer surveillance, tracking and keep watch over, marine defense, surveillance and security in public transportation structures, picture processing and interpretation, and environmental tracking. The learn types a part of the ESPRIT venture DIMUS (Data Integration in Multisensor Systems).
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Additional resources for Data Fusion Applications: Workshop Proceedings Brussels, November 25, 1992
L. Stroobants, "State of the Art of Geographic Information Systems and System Specifications for Integrability", Rigel Engineering, TRACS deliverable WP4, Nov. 1992. ESPRIT Project AZZURRO: Data Fusion for Marine Protection Susanna Ghelfo Agusta Sistemi Tradate (Varese) Italy Abstract This paper will present the objectives of the AZZURRO project from the application point of view. This project is devoted to the development of advanced software techniques for the fusion of environmental data collected by two different sensors: an active sensor such as a Lidar Time Resolved Laser Fluorosensor (TRLF) and a passive one such as a multispectral scanner (Daedalus).
The similarities between the subjects of the monitoring tasks lead to a set of subject types, called categories. These categories have been refined and structured into static, dynamic and complex aspects which build the Category Model. With the Category Model, a framework for the representation of the subjects of monitoring tasks is given. The process of infonnation fusion uses the model for classifying input in finding a category for different types of infonnation. While numeric data can be used for enhancing the attributes of the category elements, symbolic infonnation, as provided by human sources, can directly represented as an category element.
The first step towards a generalized monitoring model is the analysis of systems used in different monitoring application areas. Sometimes, this process is called (technical) domain analysis. Functional similarities of the systems are being analyzed and lead to a number of fundamental monitoring tasks. These monitoring tasks represent the functions which are common to most of the analyzed systems. What application areas have been analyzed and what the fundamental tasks are, is subject of the next section.