Approaching Semantically-Mediated Acoustic Data Fusion

Our primary hypothesis is that it should be possible to enrich data fusion by semantic processing, with wide potential application. In order to achieve our aim we need to represent the semantic data and enable reasoning about it in a framework that can be aligned with data fusion. Ontologies are most suited to this task as they allow for rich representation of data structure; some approaches include probabilistic representation. These can be aligned with data fusion approaches, such as Bayesian, which can fuse by including estimates of uncertainty. We shall describe our initial approaches towards establishing our hypothesis, including a survey of the enabling technologies, a description of application data (acoustic sensors, military scenario), and our new method of feature selection for acoustic data fusion. We also explore the semantic attributes and the representations that can be deployed for enrichment purposes, showing how ontologies can be used in this context. In these respects we shall show how we can approach enrichment of data fusion by semantic technologies, how this can capitalise on the current stock of techniques, and illustrate the potential benefits associated with this new approach.