Decision makers operating in modern defence theatres need to comprehend and reason with huge quantities of potentially uncertain and imprecise data in a timely fashion. An automatic information fusion system is developed which aims at supporting a commander's decision making by providing a threat assessment, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled by a network of entities and relationships between them, while the uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. To support the implementation of the threat assessment functionality, an efficient valuation-based reasoning scheme, referred to as an evidential network, is developed. To reduce computational overheads, the scheme performs local computations in the network by applying an inward propagation algorithm to the underlying binary join tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the binary join tree.

The paper addresses the problem of estimating the launch and impact points of a ballistic target from radar measurements. The problem has been faced under different hypotheses on the available prior knowledge. The proposed approach combines a nonlinear batch estimator with a recursive MM (multiple model) particle filter in order to attain the estimation goal. Extensive simulations assess the achievable estimation performance.