Paper
15 September 2004 Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment
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Abstract
The dog's nose is an effective, highly-mobile sampling system, while the canine olfactory organs are an extremely sensitive detector. Having been trained to detect a wide variety of substances with exceptional results, canines are widely regarded as the 'gold standard' in chemical vapor detection. Historically, attempts to mimic the ability of dogs to detect vapors of explosives using electronic 'dogs noses' has proven difficult. However, recent advances in technology have resulted in development of detection (i.e., sampling and sensor) systems with performance that is rapidly approaching that of trained canines. The Nomadics Fido was the first sensor to demonstrate under field conditions the detection of landmines with performance approaching that of canines. More recently, comparative testing of Fido against canines has revealed that electronic vapor detection, when coupled with effective sampling methods, can produce results comparable to that of highly-trained canines. The results of these comparative tests will be presented, as will recent test results in which explosives hidden in cargo were detected using Fido with a high-volume sampling technique. Finally, the use of canines along with electronic sensors will be discussed as a means of improving the performance and expanding the capabilities of both methods.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Fisher, John Sikes, and Mark Prather "Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment", Proc. SPIE 5403, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, (15 September 2004); https://doi.org/10.1117/12.542900
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Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Explosives

Explosives detection

Statistical analysis

Particles

Land mines

Sensor performance

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