Intelligent cooperative control for urban tracking with Unmanned Air Vehicles

We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of Unmanned Air Vehicles (UAVs). We extend the work of Yu et. al. [1] to add a machine learning component that uses observations of target position to learn a model of target motion. Our learner is the Sequence Memoizer [2], a Bayesian model for discrete sequence data, which we use to predict future target location identifiers, given a context of previous location identifiers. Simulated cooperative control of a team of 3 UAVs in a 100-block city filled with various sizes of buildings verifies that learning a model of target motion can improve target tracking performance.