Imitating Radar Operator Decisions for Maritime Surveillance Missions Using Bayesian Networks

This paper presents the use of Bayesian networks for learning decisions made by a human radar operator carrying out a maritime surveillance mission. By imitating the operator's decisions, a significant increase in the autonomy of a radar surveillance system can be achieved as well as potentially streamlining the qualification process. For maritime scenarios, current literature has only focused on using a Bayesian network (BN) for identification and assessment, and often assumes inputs from a generic surveillance sensor. Furthermore, in both the maritime surveillance and radar operations domain, there has been no investigation into using the operator's data in order to learn the decisions made throughout the mission. This paper uses a realtime radar simulation in order to obtain the scenario and radar information that would be observed by a human operator. In conjunction with a user interface, the simulation is further used to obtain operator decisions for a given mission with the maximum likelihood approach used to obtain the BN probabilities. The BN is then used in place of the operator for interfacing with the simulation in order to test the suitability of this method. Several typical scenarios are used to demonstrate the BN's operational ability relative to that of the operator. Additionally, the required data size for sufficient performance is investigated.