Benefits Of Soft Sensors And Probabilistic Fusion

Dennis M. Buede; Edward L. Waltz

doi:10.1117/12.960363

5 September 1989 Benefits Of Soft Sensors And Probabilistic Fusion

Dennis M. Buede, Edward L. Waltz

Proceedings Volume 1096, Signal and Data Processing of Small Targets 1989; (1989) https://doi.org/10.1117/12.960363
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

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.

Citation Download Citation

Dennis M. Buede and Edward L. Waltz "Benefits Of Soft Sensors And Probabilistic Fusion", Proc. SPIE 1096, Signal and Data Processing of Small Targets 1989, (5 September 1989); https://doi.org/10.1117/12.960363

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