Sparse Approximation based Maximum Likelihood Approach for Radiological Source Terms Identification
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A computationally efficient and accurate method is presented for identifying number, intensity and location of stationary multiple radiological sources. This method makes the use of a grid generated by discretizing the region of interest for source locations and forms a sparse convex optimization problem (SCOP) based on L1-norm minimization. The solution to SCOP contains all information needed for source terms identification; the value of the nonzero elements in the solution vector approximates the source intensity, the corresponding grid points to the nonzero elements approximates the source locations and the number of nonzero elements exactly corresponds to 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 SCOP based maximum likelihood estimation (SCOPMLE) is verified using real experimental data acquired during radiological field trials in the presence of up to three point sources of gamma radiation. The numerical results show that SCOPMLE efficiently and accurately identifies the source terms simultaneously, and it outperforms existing methods which have been used for stationary multiple radiological source terms identification.