Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. We also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, an interferometric radar altimeter (IRA) is used to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real-time on board. Finally, the performance of the proposed system is verified through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

In order to improve the performance of Terrain Referenced Navigation (TRN), an Interferometric Radar Altimeter (IRA) has been developed as a more accurate altimeter. The IRA outputs not only the relative distance (slant range, R) but also the cross-track angle (look angle, θ) of the closest point on the zero Doppler line by using the principle of interferometry and two or more antennas. To perform TRN using the IRA, the 3D relative position of the closest point should be calculated. There is a formula to calculate the relative position of the closest point using the Euler angles. However, in an actual flight environment in which the influence of wind exists, the angle of attack, the side slip angle and “the effective look angle” should be used rather than the Euler angles. In this paper, a new formula for calculating the relative position of the closest point is proposed and mathematically derived. The proposed formula was verified with real data from actual flight. The flight test results show that the positions of the closest points calculated using the conventional method and the proposed method are different because of the wind effect. The TRN simulation results indicate that the proposed formula calculates the closest points more accurately than the conventional formula.

In Global Navigation Satellite System (GNSS)-denied environments, unmanned aerial vehicles (UAVs) relying on Vision-Based Navigation (VBN) in high-altitude, mountainous terrain face severe challenges due to geometric distortions in aerial imagery. This paper proposes a georeferenced localization framework that integrates orthorectified aerial imagery with Scene Matching (SM) to achieve robust positioning. The method employs a camera projection model combined with Digital Elevation Model (DEM) to orthorectify UAV images, thereby mitigating distortions from central projection and terrain relief. Pre-processing steps enhance consistency with reference orthophoto maps, after which template matching is performed using normalized cross-correlation (NCC). Sensor fusion is achieved through extended Kalman filters (EKFs) incorporating Inertial Navigation System (INS), GNSS (when available), barometric altimeter, and SM outputs. The framework was validated through flight tests with an aircraft over 45 km trajectories at altitudes of 2.5 km and 3.5 km in mountainous terrain. The results demonstrate that orthorectification improves image similarity and significantly reduces localization error, yielding lower 2D RMSE compared to conventional rectification. The proposed approach enhances VBN by mitigating terrain-induced distortions, providing a practical solution for UAV localization in GNSS-denied scenarios.