  Individual tracking of museum visitors based on portable radio beacons, an
asset for behavioural analyses and comfort/performance improvements, is seeing
increasing diffusion. Conceptually, this approach enables room-level
localisation based on a network of small antennas (thus, without invasive
modification of the existent structures). The antennas measure the intensity
(RSSi) of self-advertising signals broadcasted by beacons individually assigned
to the visitors. The signal intensity provides a proxy for the distance to the
antennas and thus indicative positioning. However, RSSi signals are well-known
to be noisy, even in ideal conditions (high antenna density, absence of
obstacles, absence of crowd, ...). In this contribution, we present a method to
perform accurate RSSi-based visitor tracking when the density of antennas is
relatively low, e.g. due to technical constraints imposed by historic
buildings. We combine an ensemble of "simple" localisers, trained based on
ground-truth, with an encoding of the museum topology in terms of a
total-coloured graph. This turns the localisation problem into a cascade
process, from large to small scales, in space and in time. Our use case is
visitors tracking in Galleria Borghese, Rome (Italy), for which our method
manages >96% localisation accuracy, significantly improving on our previous
work (J. Comput. Sci. 101357, 2021).
