Assessment of the Practicality of Pulsed Fast Neutron Analysis for Aviation Security Summary (2002) / Chapter Skim
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Executive Summary
Executive Summary
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PFNA can determine the carbon, nitrogen, and oxygen content in an object, and the relative amounts of these elements can then be used to help discriminate explosive from non-explosive materials. The detection algorithms exploit the high nitrogen and oxygen content of most explosives and the high chlorine content and high carbon-to-oxygen ratio in certain drugs.3 The strengths of PFNA are its ability to penetrate deep into nonhydrogenous cargos, its ability to discriminate on the basis of the elemental composition of the interrogated material, and its ability to penetrate metal objects within the cargo.
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The results of the test conducted by the FAA in January 1997 showed an overall detection rate of 75 percent and an estimated false alarm rate of 13 percent. At that time, SAIC stated that the existing DARPA PFNA system, with further hardware modifications, should be capable of achieving a detection efficiency of 90 percent or better for explosives of the size required for certifications of luggage inspection systems, a throughput rate of two containers per hour, and a false alarm rate of less than 10 percent of containers inspected.
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considered to be unacceptable for any system applied to the detection of explosives in break bulk, owing to the inadequate probability of detection and the long time required to resolve the high level of false alarms. Although PFNA has the greatest potential for detecting explosives in containerized cargo, the PFNA technology not only fails to meet the current FAA cargo detection metric)
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The third condition is that PFNA must either reduce its false alarm rate to less than 1 percent (while maintaining the FAA cargo detection metric) or be associated with an acceptable false alarm resolution procedure that can clear a cargo container of a false alarm within 5 minutes.
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Current blind testing protocols used in cargo testing, although carefully designed with test time and cost in mind, are not sufficient when the physics underlying the detection algorithms are not clearly understood. The current PFNA prototype has two different detection algorithms, one based on a neural net and one on a discriminate analysis.
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These insertion technologies should only be used in PFNA blind testing, and no record of the insertion methods or of the resulting PFNA detection data should be retained for, or available to, any neural net training. All neural net training data should have a completely independent pedigree (including being constructed by a different set of people)
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In fact, the apparently strong dependence of the current explosives-detection algorithm on the nitrogen content of an explosive suggests that this multielement detection capability is not being fully exploited by the current explosives-detection algorithms. While it may be that the natural variation in the explosive composition of current threats prevents other elementspecific detection metrics that do not focus on nitrogen from playing an important role, this multielement detection capability should be quantified and its potential importance in detecting other non-nitrogenous-based explosives and nonchlorinated contraband materials (e.g., currency, mustard and nerve chemical agents, and hazardous industrial chemicals)
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