A critical assessment of two methods for heterogeneous information fusion

Data fusion in heterogeneous environments plays a major role in assisting end users by providing them with an increased situational awareness so that decisions can be made about events in the field. Heterogeneous fusion involves combining different types of soft and hard data such that the situation or the resulting output is more precise, accurate, complete or easy to comprehend by decision makers. If soft data conveys more sophisticated information that is difficult to measure and hard data can be described with specificity, the question of how to take advantage of their complementarities is attracting considerable attention from data fusion community. This paper presents two methods for heterogeneous fusion, differing in procedures used to combine information items. We propose two methods that enrich a situation by adding supplementary attributes to entities, so that entities have a better characterisation. A domain ontology and reasoning capacities support both methods, although they implement different enrichment solutions. First, a picture of entities and relationships is created by using only hard data provided by sensors and then this picture is enriched thanks to soft data, in the form of succinct or more complex observation reports. The enrichment allows the situation to be understood and processed in a meaningful way by end users; however uncertainty arises as various items are matched. The paper also discusses underlying uncertainties induced by both methods along criteria of the current URREF framework.