Efficient Bayesian methods for updating and storing uncertain search information for UAVs

Algorithms for the autonomous decision and control of unmanned aerial vehicles (UAV´s) require access to accurate information about the state of the environment in order to perform well. However, this information is oftentimes uncertain and dynamically changing. An efficient method to store and retrieve this information in such circumstances is provided in this paper. Bayesian methods are used to take probabilistic information about reports of object detections and to incorporate this information into an information base, which includes probabilities of the location and probabilities for an object´s existence. This allows for the discrimination of false/real objects, with the easy extension into false/real targets. The resulting process is suited for cooperative search by a team of UAVs.