An optimal sensor management technique for Unmanned Aerial Vehicles tracking multiple mobile ground targets

In this paper, we present an optimal sensor management technique for an Unmanned Aerial Vehicle (UAV) to autonomously geo-localize multiple mobile ground targets. The target states are continuously estimated using target locations asynchronously captured by a gimbaled camera with a limited field of view and processed with a set of Extended Kalman Filters (EKFs). The technique incorporates a Dynamic Weighted Graph (DWG) method to first group estimated targets and then determine regions with high target densities. A Model Predictive Control (MPC) method is used to compute a camera pose that minimizes the overall uncertainty of the target state estimates. The validity of the proposed technique is demonstrated using simulation results.