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.

No abstract

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.

No abstract

A calibration method for a strap-down inertial navigation system (SDINS) is presented here. The body-frame drift induced by the variation of the accelerometer sensing axe due to temperature changes is an important source of navigation error not considered in conventional calibration methods. In this paper, we propose an advanced calibration method that can compensate for SDINS error sources including not only bias, scale factor, and misalignment but also the body-frame drift by utilizing a 2-axis turntable. The body-frame drift is estimated using horizontal accelerometer measurements during a calibration procedure. Simulation results show that the body-frame drift behaves like an additional gyro bias error and that the proposed calibration technique can significantly improve pure navigation performance by removing the effect of body-frame drift.y x C = Direction cosine matrix relating a vector's components in x frame to y frame C a = Acceleration input into the C frame C w = Angular rate input into the C frame C i a δ = i-axis acceleration error in the C frame C i w δ = i-axis angular rate error in the C frame C i a = i-axis true specific force in the C frame C i w = i-axis true angular rate in the C frame i α = i-axis accelerometer bias ii α = i-axis accelerometer scale factor error ij α = i-axis accelerometer misalignment toward jaxis (i ≠ j) i β = i-axis gyro bias ii β = i-axis gyro scale factor error ij β = i-axis gyro misalignment toward j-axis (i ≠ j) R′ = Pseudo-reference frame determined by the offset angular position of a turntable T B =Table body frame M B = Body frame of mount fixture installed between the SDINS and the turntable ' T T B R B w = Angular rate of the turn table R IR w = Earth rate Γ = Initial angular position error matrix of the turntable M = Misalignment matrix between the IMU body frame and the turntable body frame a A = Non-orthogonal sensing axes vector of accelerometer triad g A = Non-orthogonal sensing axes vector of gyro triad a A R ε = Misalignment matrix between A a and R g A R ε = Misalignment matrix between A g and R X = Calibration coefficient vector Z = Measurement vector H = Observation matrix