Gamma-Ray Imaging with Spatially Continuous Intensity Statistics

Marcus Greiff, Emil Rofors, Anders Robertsson, Rolf Johansson, Rikard Tyllström

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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.
Original languageEnglish
Title of host publicationProc. 2021 IEEE/RSJ Int. Conf.Intelligent Robots and Systems (IROS2021), Sep 27 - Oct 1, 2021, Prague, Czech Republic
Pages5234-5239
Number of pages6
Publication statusPublished - 2021 Sep
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Prague, Czech Republic
Duration: 2021 Sep 272021 Oct 1

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
Country/TerritoryCzech Republic
CityPrague
Period2021/09/272021/10/01

Subject classification (UKÄ)

  • Control Engineering

Fingerprint

Dive into the research topics of 'Gamma-Ray Imaging with Spatially Continuous Intensity Statistics'. Together they form a unique fingerprint.

Cite this