Applications of scheduling theory to the optimal intelligence gathering of a military surveillance aircraft

Abstract

This paper describes efforts to develop a simulation environment for modeling the operations of an airborne surveillance aircraft. The simulated aircraft will monitor seismic and visual sensors to detect disturbances on the ground. In traditional systems, a human operator is responsible for managing all surveillance resources. The simulation environment developed in this project will allow for the evaluation of automated scheduling systems. The system described here uses an on-line optimization algorithm to schedule cameras to targets. To assess the performance of the on-line optimization algorithm, the team developed an off-line formulation of the scheduling problem to estimate an upper-bound for the optimal achievable performance. Varying both the total area defined by the user as areas of interest and those areas' corresponding priority, we assessed the intelligence-gathering capability of the system using a performance ratio. By experimentally comparing the performance ratio between the on-line algorithm's performance and the optimal achievable off-line performance, we conclude that the human operators can detriment the performance of the on-line scheduling algorithm.