On the Theory of Particle Count Detection with an Application to the Triggering of Biological Warfare Detection Systems


  1. Yee, E.
Corporate Authors
Defence Research Establishment Suffield, Ralston ALTA (CAN)
A new procedure is presented for the detection of a bio-target signal in aerosol particle number count data when no prior knowledge of the existence of such a signal or of its characteristics (e.g., amplitude and shape) is assumed. Unlike previous bio-target detection algorithms, the algorithm in this paper is derived rigorously by the direct application of probability theory. To address the detection problem, probability theory is used to compare two models (or hypotheses); namely, a model (M1) that postulates the presence of the background interference only, and an alternative model (M2) that postulates the presence of a bio-target signal in the background interference. The posterior probability for each model is calculated based on all the available prior information, and used to determine the posterior odds rato O21 in favor of model M2 over model M1. This ratio provides a quantitative measure of the evidence for the presence of a bio-target signal in the data. The new detection algorithm has been applied to both simulated and real particle count data and found to perform well.
Aerosol detection;Particle counting;Bio-target signals;Detection algorithms
Report Number
DRES-TM-1999-062 — Technical Memorandum
Date of publication
01 Mar 2000
Number of Pages
Hardcopy;Document Image stored on Optical Disk

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