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Radiation source detection has seen various applications in the past decade,ranging from the detection of dirty bombs in public places to scanning criticalnuclear facilities for leakage or flaws, and in the autonomous inspection ofnuclear sites. Despite the success in detecting single point sources or a smallnumber of spatially separated point sources, most of the existing algorithmsfail to localize sources in complex scenarios with a large number of pointsources or non-trivial distributions & bulk sources. Even in simplerenvironments, most existing algorithms are not scalable to larger regionsand/or higher dimensional spaces. For effective autonomous inspection, we notonly need to estimate the positions of the sources, but also the number,distribution, and intensities of each of them. In this paper, we present anovel algorithm for the robust localization of an arbitrary distribution ofradiation sources using multi-layer sequential Monte Carlo methods coupled withsuitable clustering algorithms. We achieve near-perfect accuracy, in terms ofF1-scores (> 0.95), while allowing the algorithm to scale, both to largeregions in space and to higher dimensional spaces (5 tested).

Robust Localization of an Arbitrary Distribution of Radioactive Sources for Aerial Inspection