We present a data fusion and decision making framework to perform prioritized searching for moving objects within an environment using ground and aerial sensors. A generalized Bayesian formulation is proposed to construct a joint probabilistic representation of the current situation is used as a basis in conjunction with predefined priority information in the decision making process. To cope with the computational intractability of a full probabilistic solution, two methods of approximation were studied. Instead of maintaining the full probabilistic representation of the environment, the first method utilizes a utility function created from the initial joint probability density. The utility function is then evolved according to sensor observations taken of the environment. The second method samples the initial density and track its evolution via a particle filter. It is shown that the second method out performs the first
['Jake Toh', 'Salah Sukkarieh']
A Bayesian formulation for the prioritized search of moving objects