  Radiation source detection has seen various applications in the past decade,
ranging from the detection of dirty bombs in public places to scanning critical
nuclear facilities for leakage or flaws, and in the autonomous inspection of
nuclear sites. Despite the success in detecting single point sources or a small
number of spatially separated point sources, most of the existing algorithms
fail to localize sources in complex scenarios with a large number of point
sources or non-trivial distributions & bulk sources. Even in simpler
environments, most existing algorithms are not scalable to larger regions
and/or higher dimensional spaces. For effective autonomous inspection, we not
only need to estimate the positions of the sources, but also the number,
distribution, and intensities of each of them. In this paper, we present a
novel algorithm for the robust localization of an arbitrary distribution of
radiation sources using multi-layer sequential Monte Carlo methods coupled with
suitable clustering algorithms. We achieve near-perfect accuracy, in terms of
F1-scores (> 0.95), while allowing the algorithm to scale, both to large
regions in space and to higher dimensional spaces (5 tested).
