Benefits Of Soft Sensors And Probabilistic Fusion

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.