AAAR 34th Annual Conference
October 12 - October 16, 2015
Hyatt Regency
Minneapolis, Minnesota, USA
Abstract View
SenseNet – Performance Modeling of an Outdoor Biothreat Detection System
WILLIAM HARRIS, Ray Pierson, Cody Niese, Egbert Tse, Dave Wasson, Jonathan Thornburg, Quentin Malloy, Prakash Doraiswamy, Robert Serino, Northrop Grumman Inc.
Abstract Number: 631 Working Group: Bioaerosols
Abstract SenseNet is a city-wide system designed to detect biothreats. Detection is determined through the use of advanced learning algorithm which leverages a large number of low cost sensors to improve detection and confidence. A system performance model was developed to understand the detection capability and aid in determining the optimum number and placement of sensors to improve detection and confidence. The model is built around a simulator that runs the advanced learning algorithm for a given sensor configuration along with a simulated plume and a statistical background model. The background was provided by correlating sensor performance outdoors with data obtained from EPA’s AirNow sensors. Supported sensors include fluorescence single particle counters, aerosol LIDAR, and the MicroPEM point sensor. The system performance model was run for a range of environmental conditions and plume release scenarios to give the overall estimated performance of the system. Typically the system performance model runs one hundred simulations for a given number of randomly located sensors. The average time-to-detection and probability of detection were calculated. These results were used to determine the SenseNet’s initial sensor density and diversity for deployment based on a given area.