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Proceedings Paper

Does extra information always help in data fusion?
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Paper Abstract

Adding new sensor metric information into a data fusion process does not always improve performance and can sometimes produce poorer results. References 1 and 2 used examples to show that - in some instances and contrary to expectation - adding new information resulted in poorer rather than improved performance, even though the information itself was correct. Correct is being used here to describe data that may be in error because of sensor deficiencies but whose error characteristics are accurately described and known to the fusion process. In other words, the fusion process is not being lied to be misrepresentation of the data quality. In this sense, an individual data point may be inaccurate, but the fusion process is capable of properly weighting that point in an optimal sensors that its statistical inaccuracy does not damage the final product any more than a data point from a better sensor that has less statistical inaccuracy. In a multiple-sensor fusion process, these kinds of result have been cited as reasons for not using data from poorer quality sensors for fear of diluting the performance of the better quality sensors. This paper explores the counterintuitive findings for these referenced examples and evaluates under what conditions lesser quality sensor or sensor that mistakenly overestimate their own data quality should be allowed to contribute to a sensor fusion process.

Paper Details

Date Published: 4 October 1999
PDF: 11 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364033
Show Author Affiliations
Albert J. Perrella, Institute for Defense Analyses (United States)
Charles W. Glover, Oak Ridge National Lab. (United States)
Steven Waugh, Ballistic Missile Defense Organization (United States)

Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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