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Sparse Approximation-Based Maximum Likelihood Approach for Estimation of Radiological Source Terms
Authors
Lee, T., Singla, P, Singh, T. and Gunatilaka, A.
Source
IEEE Transactions on Nuclear Science 63(2).
Abstract
A computationally efficient and accurate method is
presented for identifying the number, intensity and location of
stationary multiple radiological sources. The proposed method
uniformly grids the region of interest resulting in a finite set of
solutions for the source locations. The resulting problem is a sparse
convex optimization problem based on L1-norm minimization.
The solution of this convex optimization encapsulates all information
needed for the estimation of source terms; the values of the
nonzero elements of the solution vector approximates the source
intensity, the grid points corresponding to the nonzero elements
approximates the source locations, and the number of nonzero
elements is the number of sources. The accuracy limited by the
resolution of the grid is further improved by making use of the
maximum likelihood estimation approach. The performance of
sparse approximation based maximum likelihood estimation is
verified using real experimental data acquired from radiological
field trials in the presence of up to three point sources of gamma
radiation. The numerical results show that the proposed approach
efficiently and accurately identifies the source terms simultaneously,
and it outperforms existing methods which have been used
for stationary multiple radiological source terms estimation.
@article{Lee2016,
title = "Sparse Approximation-Based Maximum Likelihood Approach for Estimation of Radiological Source Terms",
journal = "IEEE Transactions on Nuclear Science",
volume = "63",
number = "2",
pages = "1169--1187",
year = "2016",
author = "Lee, T., Singla, P, Singh, T. and Gunatilaka, A.",
keywords = "Maximum likelihood estimation, parameter estimation,
radiation monitoring, radioactive materials"
}
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