Sparse Approximation-Based Maximum Likelihood Approach for Estimation of Radiological Source Terms


A computationally efficient and correct methodology is presented for identifying the amount, intensity and placement of stationary multiple radiological sources. The proposed technique uniformly grids the region of interest ensuing in a very finite set of solutions for the source locations. The ensuing problem is a sparse convex optimization problem based mostly on -norm minimization. The solution of this convex optimization encapsulates all data required for the estimation of source terms; the values of the nonzero components of the answer vector approximates the supply intensity, the grid points comparable to the nonzero elements approximates the supply locations, and the quantity of nonzero components is the quantity of sources. The accuracy limited by the resolution of the grid is more improved by making use of the most likelihood estimation approach. The performance of sparse approximation based maximum probability estimation is verified using real experimental data acquired from radiological field trials in the presence of up to a few 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 ways that are used for stationary multiple radiological source terms estimation.

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