Making Command and Control Maps More Useful for Operators of Unmanned Aerial Systems: A Preliminary Model and Empirical Approach

Operators of military unmanned aerial systems (UASs) work in highly dynamic environments. Their main focus is on mission management via the UAS's payload, yet, they continuously interact with the command and control (C2) map to obtain situation awareness and decision-making. Since C2 maps are cluttered and overloaded with information, we aim to develop a machine-learning based spatial-temporal algorithm that will identify the most relevant information items to the UAS operator and deliver the right visualized information on the C2 map. Towards this algorithm development, simulated experiments for collecting user-based importance data were conducted and analyzed. Results show high prediction model feasibility, allowing to move forward with the next phases of algorithm development in future research.