Application of Bayesian statistical analysis to illicit substance detection using nondestructive interrogation techniques

Leonid Sagalovsky; Donald L. Smith; Bradley J. Micklich; Charles L. Fink; Thomas J. Yule

doi:10.1117/12.267906

27 February 1997 Application of Bayesian statistical analysis to illicit substance detection using nondestructive interrogation techniques

Leonid Sagalovsky, Donald L. Smith, Bradley J. Micklich, Charles L. Fink, Thomas J. Yule

Author Affiliations +

Proceedings Volume 2867, International Conference Neutrons in Research and Industry; (1997) https://doi.org/10.1117/12.267906
Event: Fifth International Conference on Applications of Nuclear Techniques: Neutrons in Research and Industry, 1996, Crete, Greece

Abstract

Non-destructive interrogation systems designed to locate illicit substances in sealed containers involve decision making when the available objective information is incomplete. The greater the quantity of information, the more reliable is the determination of the unknown content. Therefore, it is important to be able to utilize all possible measured data pertaining to the unknown object. Among the data which can be considered are x-ray measurements, fast-neutron transmission measurements, cargo manifest data and, possibly, information of a physical, chemical or even psychological nature. The Bayesian approach provides a statistical framework for merging diverse information about any object, including a priori knowledge, subjective knowledge and objective knowledge gained from current measurements. This paper outlines the fundamental principles of Bayesian analysis and explores possible applications to the detection of illicit substances.

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Leonid Sagalovsky, Donald L. Smith, Bradley J. Micklich, Charles L. Fink, and Thomas J. Yule "Application of Bayesian statistical analysis to illicit substance detection using nondestructive interrogation techniques", Proc. SPIE 2867, International Conference Neutrons in Research and Industry, (27 February 1997); https://doi.org/10.1117/12.267906

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