Toward efficient quality of information estimation in simultaneous acoustic tracking and classification of multiple targets

An individual sensor's information output is often insufficient for an application, with ambiguities that require refinement or corroboration by fusion with information from other sensors. Fusion of multiple information sources is performed to create an information product of higher quality of information (QoI) that supports more effective intelligence, surveillance, and reconnaissance (ISR). In this paper we present an approach to determining the QoI attributes (metadata) relevant to tracking and classification of multiple vehicles, and the necessary weighting (qualifying) terms, as information derived from multiple acoustic sensors is fused. Field trial data is used to validate the conclusions.