Improving cooperative tracking of an urban target with target motion model learning

Tracking a ground urban target with multiple unmanned aerial vehicles (UAVs) is a challenging problem due to cluttered urban environments and coordination of nonholonomic UAV motion. Our previous work has demonstrated in simulation that machine learning can be used in such an environment to learn a model of target motion and thereby improve tracking performance. We extend this previous work by creating a more realistic simulation using road network and building height data extracted from downtown San Diego. We demonstrate effectiveness of target motion model learning in the new simulation environment. Additionally, we demonstrate performance improvement by extending the algorithm used to coordinate the UAVs for tracking the urban target.