  In the field of structural health monitoring (SHM), the acquisition of
acoustic emissions to localise damage sources has emerged as a popular
approach. Despite recent advances, the task of locating damage within composite
materials and structures that contain non-trivial geometrical features, still
poses a significant challenge. Within this paper, a Bayesian source
localisation strategy that is robust to these complexities is presented. Under
this new framework, a Gaussian process is first used to learn the relationship
between source locations and the corresponding difference-in-time-of-arrival
values for a number of sensor pairings. As an acoustic emission event with an
unknown origin is observed, a mapping is then generated that quantifies the
likelihood of the emission location across the surface of the structure. The
new probabilistic mapping offers multiple benefits, leading to a localisation
strategy that is more informative than deterministic predictions or
single-point estimates with an associated confidence bound. The performance of
the approach is investigated on a structure with numerous complex geometrical
features and demonstrates a favourable performance in comparison to other
similar localisation methods.
