Thermal bioaerosol cloud tracking with Bayesian classification

Christian W. Smith; Julia R. Dupuis; Elizabeth C. Schundler; William J. Marinelli

doi:10.1117/12.2261699

3 May 2017 Thermal bioaerosol cloud tracking with Bayesian classification

Christian W. Smith, Julia R. Dupuis, Elizabeth C. Schundler, William J. Marinelli

Proceedings Volume 10183, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVIII; 101830I (2017) https://doi.org/10.1117/12.2261699
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Citation Download Citation

Christian W. Smith, Julia R. Dupuis, Elizabeth C. Schundler, and 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); https://doi.org/10.1117/12.2261699

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Thermal modeling

Thermography

Atmospheric modeling

Motion models

Sensors

Data modeling

Imaging systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years