The NTIS website and supporting ordering systems are undergoing a major upgrade from 8PM on September 25th through approximately October 6. During that time, much of the functionality, including subscription and product ordering, shipping, etc., will not be available. You may call NTIS at 1-800-553-6847 or (703) 605-6000 to place an order but you should expect delayed shipment. Please do NOT include credit card numbers in any email you might send NTIS.
Documents in the NTIS Technical Reports collection are the results of federally funded research. They are directly submitted to or collected by NTIS from Federal agencies for permanent accessibility to industry, academia and the public.  Before purchasing from NTIS, you may want to check for free access from (1) the issuing organization's website; (2) the U.S. Government Printing Office's Federal Digital System website http://www.gpo.gov/fdsys; (3) the federal government Internet portal USA.gov; or (4) a web search conducted using a commercial search engine such as http://www.google.com.
Accession Number ADA582222
Title Model-Free Stochastic Localization of CBRN Releases.
Publication Date 2013
Media Count 16p
Personal Author I. C. Paschalidis R. T. Locke
Abstract We present a novel two-stage methodology for locating a Chemical, Biological, Radiological, or Nuclear (CBRN) source in an urban area using a network of sensors. In contrast to earlier work, our approach does not solve an inverse dispersion problem but relies on data obtained from a simulation of the CBRN dispersion to obtain probabilistic descriptors of sensor measurements under a variety of CBRN release scenarios. At its first stage, subsequent sensor observations under nominal, CBRN event-free conditions are assumed to be independent and identically distributed, and we rely on the method of types to detect a CBRN event. Conditional on such an event, subsequent sensor observations are assumed to follow a Markov process. Using composite hypothesis testing, we map sensor measurements to a source location chosen out of a discrete set of possible locations. We leverage large deviation techniques to obtain a bound on the localization probability of error and propose several methodologies for fusing sensor data to arrive at a localization decision, including a distributed one. We also address the problem of optimally placing sensors to minimize the localization probability of error. Our techniques are validated numerically using two different CBRN release simulators.
Keywords Biological warfare agents
Cbrn dispersion scenarios
Cbrn dispersion simulation
Cbrn source detection
Cbrn source localization
Chemical warfare agents
Composite hypothesis testing
Computerized simulation
Data fusion
Detection
Dispersions
Large deviations
Lattice boltzmann method
Markov processes
Nuclear weapons
Optimization
Particulates
Position finding
Quic(Quick urban & industrial complex dispersion modeling sy
Radiological warfare agents
Release detection
Scenarios
Sensor networks
Sensor placement
Sources
Urban areas
Weather


 
Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
74D - Chemical, Biological, & Radiological Warfare
63 - Detection & Countermeasures
74H - Nuclear Warfare
Corporate Author Boston Univ., MA. Dept. of Systems and Computer Engineering.
Document Type Journal article
Title Note Journal article.
NTIS Issue Number 1326
Contract Number FA8721-05-C-0002 W911NF-11-1-0227

Science and Technology Highlights

See a sampling of the latest scientific, technical and engineering information from NTIS in the NTIS Technical Reports Newsletter

Acrobat Reader Mobile    Acrobat Reader