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Proceedings Paper

Biological aerosol warning sensor model: an approach to model architecture and accelerated false alarm prediction
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Paper Abstract

Models of optically-based biological aerosol sensors may help to predict baseline performance and support efficient sensor optimization. Reducing a sensor’s false positive rate while maintaining sensitivity is an important performance goal that must be optimized. To that end, the capacity to theoretically test environmental backgrounds, in an accelerated fashion, would be valuable. Sensor false positives are presumed to occur as a result of complicated transient fluctuations in the environmental aerosol background. Simulating a sensor’s response to such naturally occurring transients, with an appropriate model, is a mechanism for accelerating sensor characterization. These models complement and reduce the need for experimentally challenging interferant tests. Additionally, validated models include the ability to characterize sensor responses to harmful agents or rare materials while simultaneously adjusting many transient parameters. We describe a model of the Lincoln Laboratory Biological Agent Warning Sensor (BAWS), highlighting our general approach to sensor model architecture. The resulting model was utilized to simulate the sensor’s response to a variety of individual background constituents as well as to time varying backgrounds with multiple constituents. The result of the simulation predicts the sensor’s false positive rate to a simulated indoor and outdoor aerosol background, which can be compared to experimental data. Model applications and improvements will be discussed.

Paper Details

Date Published: 29 December 2004
PDF: 9 pages
Proc. SPIE 5617, Optically Based Biological and Chemical Sensing for Defence, (29 December 2004); doi: 10.1117/12.578329
Show Author Affiliations
Jonathan D. Pitts, MIT Lincoln Lab. (United States)
Daniel Cousins, MIT Lincoln Lab. (United States)
Amanda Goyette, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5617:
Optically Based Biological and Chemical Sensing for Defence
John C. Carrano; Arturas Zukauskas, Editor(s)

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