Unmanned Aerial Vehicle Positioning Using a Phased Array Radio and GNSS Independent Sensors

Abstract: This thesis studies the possibility to replace the global navigation satellite system (GNSS) with a phased array radio system (PARS) for positioning and navigation of an unmanned aerial vehicle (UAV). With the increase of UAVs in both civilian and military applications, the need for a robust and accurate navigation solution has increased. The GNSS is the main solution of today for UAV navigation and positioning. However, the GNSS can be disturbed by malicious sources, the signal can either be blocked by jamming or modified to give the wrong position by spoofing. Studies have been conducted to replace or support the GNSS measurements with other drift free measurements, e.g. camera or radar systems. The position measurements from PARS alone is shown not to provide sufficient quality for the application in mind. The PARS measurements are affected by noise and outliers. Reflections from the ground makes the PARS elevation measurements unusable for this application. A root mean square error (RMSE) accuracy of 10 m for a shorter flight and 198 m for a longer flight are achieved in the horizontal plane. The decrease in accuracy for the longer flight is assumed to come from a range bias that increases with distance due to the flat earth approximation used as the navigation frame. Positioning based on PARS aided with a filter and other GNSS independent sensors is shown to reduce the noise and remove the outliers. Five filters are derived and evaluated: a constant velocity extended Kalman filter (EKF), an inertial measurement unit (IMU) aided EKF, an IMU and barometer aided EKF, a converted measurements Kalman filter (CMKF) and a stationary Kalman filter (KF). The IMU and barometer aided EKF performed the best results with a RMSE of 8 m for a shorter flight and 106 m for a longer flight. The noise is significantly reduced compared to the standalone PARS measurements. The conclusion is that PARS can be used as a redundancy system with the IMU and barometer aided EKF. If the EKF algorithm is too computational demanding, the simpler stationary KF can be motivated since the accuracy is similar to the EKF. The GNSS solution should still be used as the primary navigation solution as it is more accurate.