Gamma-Ray Imaging with Spatially Continuous Intensity Statistics
Marcus Greiff, Emil Rofors, Anders Robertsson, Rolf Johansson, Rikard Tyllström
Abstract
Novel methods for the inference of radiation intensity functions defined over known surfaces are proposed, intended for use in surveying applications with mobile spectrometers. Previous approaches, based on the maximum likelihood expectation maximization (ML-EM) framework with Poisson likelihoods, are extended to better handle spatially continuous intensity statistics using ideas from Gaussian filtering. The resulting algorithm is evaluated against a classical ML-EM method, and a recently proposed sparse additive point source localization (APSL) algorithm in a Monte-Carlo simulation study. The new generalized ASPL (GASPL) is shown to compare favorably in terms of estimation accuracy when the true intensity is not well described by a set of point sources. Finally, the GASPL is used in an experiment where a detector is mounted to an unmanned aerial vehicle to estimate the intensity and location of radioactive sources placed in a meadow.
BibTeX
@inproceedings{iros2021_gammarayimagingw,
title = {Gamma-Ray Imaging with Spatially Continuous Intensity Statistics},
author = {Marcus Greiff and Emil Rofors and Anders Robertsson and Rolf Johansson and Rikard Tyllström},
booktitle = {IROS 2021},
year = {2021}
}