Proceedings of the Human Factors & Ergonomics Society 52nd Annual Meeting, New York, NY (September 2008)
Data fusion systems are increasingly being used to support military planning and decision making. Typically these systems are designed around the current capabilities of particular data collectors (e.g., sensors) and processing algorithms. They incorporate an “ontology” that reflects the designer’s perception of the key features of the world (e.g., types of threats, classes of vehicles to be tracked) and how these can be parsed by the data fusion systems. As a consequence, they are limited in their ability to adapt to the dynamic changes that inevitably arise in the operational environment (e.g., new sensors, weapons, tactics). This is representative of a more generic problem with current approaches to system design that result in rigid systems that are unable to evolve to keep pace with changing operational conditions. In this paper, we present the results of an analysis, design, and development effort intended to move away from traditional data fusion systems towards evolvable human-in-the-loop data fusion systems. We discuss the analysis we conducted in support of an evolvable system design and provide an overview of the prototype evolvable data fusion system architecture we are developing.
1 Charles River Analytics
2 U.S. Army RDECOM CERDEC I2WD
3 Roth Cognitive Engineering
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