A Bayesian approach to the detection of small low emission sources. Academic Article

abstract

• This paper addresses the problem of detecting the presence and location of a small low emission source inside an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to reach the signal-to-noise ratio levels in the order of 10(-3). A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov chain Monte Carlo simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.

authors

• Carroll, Raymond
• Kuchment, Peter
• Mallick, Bani

published proceedings

• Inverse Probl

altmetric score

• 0.25

author list (cited authors)

• Xun, X., Mallick, B., Carroll, R. J., & Kuchment, P.

citation count

• 8

complete list of authors

• Xun, Xiaolei||Mallick, Bani||Carroll, Raymond J||Kuchment, Peter

publication date

• November 2011

publisher

• IOP Publishing  Publisher

published in

• Inverse Problems  Journal

keywords

• 65c60, 82dxx
• Math.st
• Nucl-th
• Stat.th

PubMed Central ID

• 22347764

Digital Object Identifier (DOI)

• 10.1088/0266-5611/27/11/115009

start page

• 115009

end page

• 115009

volume

• 27

issue

• 11

URL

• http://dx.doi.org/10.1088/0266-5611/27/11/115009