3 May 2017 Thermal bioaerosol cloud tracking with Bayesian classification
Author Affiliations +
Abstract
The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christian W. Smith, Julia R. Dupuis, Elizabeth C. Schundler, William J. Marinelli, "Thermal bioaerosol cloud tracking with Bayesian classification", Proc. SPIE 10183, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVIII, 101830I (3 May 2017); doi: 10.1117/12.2261699; https://doi.org/10.1117/12.2261699
PROCEEDINGS
11 PAGES


SHARE
RELATED CONTENT

Three-Dimensional Target Signature Modeling
Proceedings of SPIE (January 19 1984)
Systems Engineering Approach To Imaging Systems Assessment
Proceedings of SPIE (October 29 1981)
New Bayesian track-before-detect design and performance study
Proceedings of SPIE (September 03 1998)
Modeling of an IRST system
Proceedings of SPIE (June 27 1996)

Back to Top