The accuracy of tracking can be enhanced by the incorporation of
prior information in the form of a local road map. For instance,
the performance of ground target tracking algorithms can be
improved by incorporating hard constraints (describing roads and
junctions) into the tracking model. We describe an approach to
enhancing tracking algorithms that uses 'probability fields' to
represent the local road map information. The resulting
'Bayes-filter' equations are solved using particle filters and
compared with a basic particle filter with no additional map
information.
Bayesian networks are a powerful and convenient way of encoding expert knowledge. They can be used to infer such "high-level’ variables as "threat’ or "intent’, given observations, background and intelligence data. However, their usefulness depends on the model, i.e. the Bayesian network used for inference. We demonstrate how Bayesian multinets can be used to simplify the representation of certain complex domains, allowing a decomposition into simpler models that are conditionally independent given a class variable. We illustrate this concept using a threat assessment application, in which each component is specialised to a different class of threat and show how this simplifies model construction and target identification.
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