Data Fusion System Engineering.pdf

Data Fusion System Engineering Alan N. Steinberg Utah State University ABSTRACT The paper reports on methods for the cost-effective development and integration of multi-sensor fusion technology. The methods presented extend the Project Correlation Data Fusion Engineering Guidelines with significant evolution. The key new insight is in formulating the system engineering process as a resource management problem; allowing the application of the Bowman’s model of the duality between data fusion and resource management. DATA FUSION ENGINEERING Scope of Data Fusion given in [ 13, we define “data fusion” as a process of combining data or information to estimate or predict entity states.’ So-defined, data fusion pervades all biological cognitive activity and virtually every automated approach to the use of Following the 1998 revision to the JDL Model definition, ’ It is fairly pointless to argue whether the term data fusion or some other term is an appropriate label for this very broad concept. There is no body of common and accepted usage to which we can appeal for such specialized terms. What is important is the recognition that this broad concept is an important topic for a unified theoretical approach, and therefore deserving of its own label. Some people have prefemd terms like “information integration” with an attempt at connoting greater generality than earlier, narrower definitions of data fusion (and, perhaps, to divorce oneself from old data fusion approaches and programs). There. is danger, however, in neglecting relevant research by willful re-labeling. Author’s Current Address: Utah State University, Space Dynamics Laboratory, Logan, Utah, USA. Based on a presentation at Fusion 2000. 0885/8985/01/ $10.00 Q 2001 lEEE information. Unfortunately, the very universality of data fusion has engendered a profusion of overlapping research and development in many applications. A welter of confusing terminology and ad hoc methods in a