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Proceedings Paper

Bayesian inference and conditional probabilities as performance metrics for homeland security sensors
Author(s): Tomasz P. Jannson
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Paper Abstract

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.

Paper Details

Date Published: 4 May 2007
PDF: 9 pages
Proc. SPIE 6538, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VI, 653810 (4 May 2007); doi: 10.1117/12.718838
Show Author Affiliations
Tomasz P. Jannson, Physical Optics Corp. (United States)


Published in SPIE Proceedings Vol. 6538:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VI
Edward M. Carapezza, Editor(s)

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