We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of autonomous Unmanned Air Vehicles (UAVs). We extend the work of Yu et al. to use observations of target position to learn a model of target motion. Simulated cooperative control of a team of 9 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.