  This paper presents a technique which exploits the occurrence of certain
events as observed by different sensors, to detect and classify objects. This
technique explores the extent of dependence between features being observed by
the sensors, and generates more informed probability distributions over the
events. Provided some additional information about the features of the object,
this fusion technique can outperform other existing decision level fusion
approaches that may not take into account the relationship between different
features. Furthermore, this paper addresses the issue of coping with damaged
sensors when using the model, by learning a hidden space between sensor
modalities which can be exploited to safeguard detection performance.
