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Proceedings Paper

Benefits Of Soft Sensors And Probabilistic Fusion
Author(s): Dennis M. Buede; Edward L. Waltz
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Paper Abstract

This paper describes and quantifies the benefits of soft-decision sensors and probabilistic data fusion relative to hard-decision sensors and nonnumerical (e.g., Boolean logic) data fusion. Hard sensors measure signals and return yes/no responses (declarations) based upon decision criteria within each sensor. Soft sensors return a measure of confidence (such as a probability) that quantifies the uncertainty in detection and/or identification. These soft responses are integrated via a fusion algorithm. The composite confidence derived by fusion from all sensors is compared against a single decision criterion to make the detection/identification declaration. A soft sensor suite with Bayesian fusion is shown to provide a 30 percent increase in range at identification. This occurs only when the probabilistic uncertainty regions for sensor measurements overlap. This means more than one sensor is providing probablistic measurements at a given range for the particular target parameters.

Paper Details

Date Published: 5 September 1989
PDF: 12 pages
Proc. SPIE 1096, Signal and Data Processing of Small Targets 1989, (5 September 1989); doi: 10.1117/12.960363
Show Author Affiliations
Dennis M. Buede, Decision Logistics (United States)
Edward L. Waltz, Allied-Signal Aerospace Company (United States)

Published in SPIE Proceedings Vol. 1096:
Signal and Data Processing of Small Targets 1989
Oliver E. Drummond, Editor(s)

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