Fuel Efficient Moving Target Tracking Using POMDP with Limited FOV Sensor

Abstract

The ability to effectively track moving targets is a critical capability for future autonomous aircraft. While many methods have been developed for performing target tracking, minimal work has focused on fuel-efficient options to extend mission duration. The ability to tightly track a target is critical for certain missions; however, increased tracking errors can be accepted in certain scenarios to extend endurance. Partially Observable Markov Decision Processes (POMDPs) have been shown to be effective for tracking fixed and moving targets. This paper provides a fuel-efficient option that shows a 10% endurance increase with adequate target tracking. The algorithm provides tracking with a limited field of view fixed sensor that will have limited observations depending on mission requirements. The POMDP formulation proposed in this paper is robust enough to handle observations while also providing options for improved fuel efficiency. We perform 500 Monte Carlo simulations per configuration to provide statistical confidence in the performance of the algorithm.