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Data-driven parameter selection for activity estimation in nuclear spectroscopy
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DOI：10.1016/j.sigpro.2018.05.006.png)
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Abstract
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One of the main objectives of nuclear spectroscopy is the estimation of the counting rate of unknown radioactive sources. Recently an algorithm based on a sparse reconstruction of the time signal was proposed by the authors to estimate precisely this counting rate, and computable bounds were obtained to quantify the performances. This approach, based on a post-processed approach of a non-negative sparse regression of the time signal, relies on user-defined parameters which are difficult to set up automatically in practice. This paper presents a data-driven strategy to select the underlying parameters. The parameter controlling the sparsity of the regressor is chosen based on cross-validation, while we introduce a new, entropy-based, criterion to select the threshold parameters. Results obtained on simulations illustrate the efficiency of the proposed approach. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Compressive sensing
Sparse reconstruction
Point processes
Model selection
Adaptive methods
Spectroscopic signal processing
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