Sparse Reconstruction Algorithm for Nonhomogeneous Counting Rate Estimation

作者：Trigano Thomas^*; Sepulcre Yann; Ritov Ya'acov

来源：IEEE Transactions on Signal Processing, 2017, 65(2): 372-385.

DOI：10.1109/TSP.2016.2620104

• Statistical signal processing
• nonparametrics
• sparse signal representation
• LASSO
• nuclear spectroscopy
• activity estimation

One of the main objectives of nuclear spectroscopy is the estimation of the counting rate of unknown radioactive sources. Recently, we proposed an algorithm based on a sparse reconstruction of the time signal in order to estimate precisely this counting rate, under the assumption that it remained constant over time. Computable bounds were obtained to quantify the performances. This approach, based on a postprocessed approach of a non-negative sparse regression of the time signal, performed well even when the activity of the source was high. The purpose of this paper is to present an extension of the previous method for an activity varying over time. It relies on the same preliminary sparse reconstruction. However, the postprocessed and plugin steps are made differently to fit the nonhomogeneous framework. The adapted bounds are presented, and results on simulations illustrate the advantages and limitations of this method.

• 出版日期2017-1-15