Threat Detection of Radioactive Contraband Incorporating Compton Scattering Physics: A Model-Based Processing Approach

The detection of radioactive contraband is a critical problem in maintaining national security for any country. Gamma-ray emissions from threat materials challenge both detection and measurement technologies significantly. The development of a sequential, model-based Bayesian processor that captures...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on nuclear science 2011-02, Vol.58 (1), p.214-230
Main Authors: Candy, J V, Chambers, D H, Breitfeller, E F, Guidry, B L, Verbeke, J M, Axelrod, M A, Sale, K E, Meyer, A M
Format: Article
Language:English
Subjects:
Capture (nuclear)
Compton scattering
Contraband
Detectors
Elastic scattering
Energy measurement
Energy use
Feasibility
Materials
Medical services
Microprocessors
model-based processor
particle filter
photoelectric absorption
Photonics
Photons
physics-based approach
Representations
Scattering
sequential Bayesian processor
sequential Monte Carlo
sequential radionuclide detection
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The detection of radioactive contraband is a critical problem in maintaining national security for any country. Gamma-ray emissions from threat materials challenge both detection and measurement technologies significantly. The development of a sequential, model-based Bayesian processor that captures both the underlying transport physics of gamma-ray emissions including Compton scattering and the measurement of photon energies offers a physics-based approach to attack this challenging problem. The inclusion of a basic radionuclide representation of absorbed/scattered photons at a given energy along with interarrival times is used to extract the physics information available from noisy measurements. It is shown that this representation leads to an "extended" physics-based structure that can be used to develop an effective sequential detection technique. The resulting model-based processor is applied to data obtained from a controlled experiment in order to assess its feasibility.
ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2010.2090361