Multi agent path planning strategies based on Kalman Filter for surveillance missions

The paper is based on the definition of a path planning strategy for surveillance missions with a system of multiple Unmanned Aircraft by means of a Kalman Filter technique. The developed method aims at finding a set of commands for the network of aircraft able to minimize a cost function whose definition depends on the mission. The approach adopted in this paper comprises several steps. The first one is based on the development of a target tracking algorithm to provide information on both target and drones motion on the surveillance area by means of a Kalman Filter and Bayesian network. Then, the objective functions can be defined depending on the relative position between aircraft and target. Finally, a heuristic approach allows finding the set of commands for the aircraft deployment over the surveillance area that maximize the utility function during the mission. The results demonstrate the ability of the tracking algorithm to provide accurate estimate of the target motion and the good capability of the whole system to react to the Command centre inputs based on the defined utility functions and decision making strategy.