Multivariate data spaces and multivariable systems analysis for explosives detection systems using X rays

Details Multivariate data spaces and multivariable systems analysis for explosives detection systems using X rays Multivariate data spaces and multivariable systems analysis for explosives detection systems using X rays

Feb 1993

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993spie.1736..159f&link_type=abstract

Proc. SPIE Vol. 1736, p. 159-170, X-Ray Detector Physics and Applications, Richard B. Hoover; Ed.

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The problems of maximizing probability of detection (PD) while minimizing the probability of false alarms (PF) in the explosives device detection problem for aviation security is addressed for x-ray explosive detection systems (XREDS). Difficulties with currently available detection systems are reviewed. The basic problem lies in the use of single-hit, single-phenomenology sensor systems, where to keep PF below 10-4, the equipment threshold VT for a detection system must be so low that, at the signal-to-noise ratios (S/N) expected, we find that PD lies between 0.4 and 0.6 regardless of phenomenology or sensor. We examine the choices of signature and discriminant features for XREDS, to achieve high discrimination with few leakers (high PD) and little asset damage (low PF). That applies to low signal-to-clutter (S/C) and signal-to-jamming (S/J) ratios. We discuss choices and combinations of x-ray energies, spectral purity, direction of scattering, imaging techniques, and others. Cluster analysis, factor analysis, and principal component analysis are applied to provide effective discrimination between explosive devices and false alarm objects, thereby enhancing PD while keeping PF

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