Cooperative localization for fixed wing unmanned aerial vehicles

In this paper, we investigate how relative measurements between fixed wing unmanned aerial vehicles (UAVs) and known landmarks can be used to cooperatively localize UAVs when GPS signals are not available. A centralized Extended Kalman Filter (EKF) is used to combine local sensor information from all the UAVs to estimate the required states of all the UAVs. We compare the localization accuracy of the cooperative localization algorithm for different relative measurements such as range, bearing, range rate, and line-of-sight rate. The dynamics, IMU, airspeed, and altimeter data used in the algorithm were collected from flight tests by Naval Postgraduate School [1]. However, the relative measurements are simulated using the flight data. The results are then compared against the GPS data available from the flight test.