Unmanned aerial vehicle localization using angle of departure from a single base station and dead-reckoning

Localization in GNSS-denied environments for unmanned aerial vehicles (UAVs) has recently gained significant interest from the research community. Most of the research is focused primarily on visual localization. This paper examines an algorithm which employs angle of departure and UAVs payload sensor data for UAV localization. First the algorithm uses multiple angle of departures from a single base station and a travel calculated by applying dead-reckoning on the UAVs inertial measurement unit (IMU), to compute UAV location in 2D coordinates. The 2D location estimate is then fed into a modified extended Kalman filter, which employs the estimate, IMU and barometer data to compute the 3D coordinates for UAV. For the simulation, simulation-in-the-loop accompanied by Arducopter and MAVLink is used to simulate different trajectories and collect the required data for the algorithm. The algorithm is validated by comparing the extended Kalman filter estimates with IMU dead-reckoned positions. Three simulations were performed, consisting of linear, zigzag, and curved trajectories. A (Formula presented.) percentile error of 2.5 and 4 m is achieved for the x-coordinate and y-coordinate, respectively, on the zigzag and curved trajectories. Interestingly, the linear trajectory showed a larger localization error in its y-coordinate.