Bayesian inference and conditional probabilities as performance metrics for Homeland Security sensors

摘要

This paper discusses military and Homeland Security sensors, sensor systems, and sensor fusion under very general assumptions of statistical performance. In this context, the system performance metrics parameters are analyzed in the form of direct and inverse conditional probabilities, based on so-called signal theory, applied first for automatic target recognition (ATR). In particular, false alarm rate, false positive, false negative rate, accuracy, and probability of detection (or, probability of correct rejection), are discussed as conditional probabilities within classical and Bayesian inference. Several examples from various homeland security areas are also discussed to illustrate the concept. As a result, it is shown that vast majority of sensor systems (in a very general sense) can be discussed in terms of these parameters.