  This paper considers the problem of low-dimensional visualisation of very
high dimensional information sources for the purpose of situation awareness in
the maritime environment. In response to the requirement for human decision
support aids to reduce information overload (and specifically, data amenable to
inter-point relative similarity measures) appropriate to the below-water
maritime domain, we are investigating a preliminary prototype topographic
visualisation model. The focus of the current paper is on the mathematical
problem of exploiting a relative dissimilarity representation of signals in a
visual informatics mapping model, driven by real-world sonar systems. An
independent source model is used to analyse the sonar beams from which a simple
probabilistic input model to represent uncertainty is mapped to a latent
visualisation space where data uncertainty can be accommodated. The use of
euclidean and non-euclidean measures are used and the motivation for future use
of non-euclidean measures is made. Concepts are illustrated using a simulated
64 beam weak SNR dataset with realistic sonar targets.
