Sparse Regression Algorithm for Activity Estimation in \gamma Spectrometry

• Published in: IEEE transactions on signal processing Vol. 61; no. 17; pp. 4347 - 4359
• Main Authors: Sepulcre, Yann, Trigano, Thomas, Ritov, Ya'acov
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; compressed sensing; Computer simulation; Counting; Detection, estimation, filtering, equalization, prediction; Dictionaries; Distortion measurement; Estimates; Estimation; Exact sciences and technology; Information, signal and communications theory; Parameter estimation; Photonics; Photons; Real-time systems; Regression; Sampling, quantization; Shrinkage; Signal analysis; Signal and communications theory; Signal processing algorithms; Signal representation. Spectral analysis; Signal, noise; Sparse approximation; Spectrometers; Spectroscopy; statistical analysis; Telecommunications and information theory; Performance evaluation; Dictionaries; Parameter estimation; Far field; Algorithm; Poisson process; Direct method; Statistical method; Arrival time; Spectrometry; Simulation; spectroscopy; Signal processing; statistical analysis; Signal analysis; Compressed sensing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2013.2264811

We consider the counting rate estimation of an unknown radioactive source, which emits photons at times modeled by an homogeneous Poisson process. A spectrometer converts the energy of incoming photons into electrical pulses, whose number provides a rough estimate of the intensity of the Poisson process. When the activity of the source is high, a physical phenomenon known as pileup effect distorts direct measurements, resulting in a significant bias to the standard estimators of the source activities used so far in the field. We show in this paper that the problem of counting rate estimation can be interpreted as a sparse regression problem. We suggest a post-processed, non-negative, version of the Least Absolute Shrinkage and Selection Operator (LASSO) to estimate the photon arrival times. The main difficulty in this problem is that no theoretical conditions can guarantee consistency in sparsity of LASSO, because the dictionary is not ideal and the signal is sampled. We therefore derive theoretical conditions and bounds which illustrate that the proposed method can none the less provide a good, close to the best attainable, estimate of the counting rate activity. The good performances of the proposed approach are studied on simulations and real datasets.