Gyro-stellar inertial attitude estimation for satellite with high motion rate

Abstract

For a common micro-satellite, orbiting in a circular sun-synchronous orbit (SSO) at an altitude between 500 and 600km, the satellite attitude during off-nadir imaging and staring-imaging operations can be up to ±45 degree on roll and pitch angles. During these off-nadir pointing for both multi-trip operation and staring imaging operations, the spacecraft body is commonly subject to high-rate motion. This posts challenges for a spacecraft attitude determination subsystem called Gyro Stellar Inertial Attitude Estimate (GS IAE), which employs gyros and star sensors to maintain the required attitude knowledge, since star trackers will severely degrade attitude estimation accuracies when the spacecraft is subject to high-rate motion. This paper analyses the star motion-induced errors for a typical star tracker, models the star motion-induced errors to assess the performance impact on the attitude estimation accuracy, and investigates the adaptive extended Kalman filter design in the GS IAE while evaluating its effectiveness.

Signal corrector and decoupling estimations for UAV control

“…From the proof of Theorem 1, the systems are finite time stable, and their approximations are asymptotically stable according to (17) and (18). According to the analysis in time and frequency domains, the system stability and robustness have the following properties:…”

“…In order to reduce the disturbances/errors in position/attitude angle, KF (Kalman filter) or EKF (extended Kalman filter) is adopted for signals fusion to restrict the defects of individual measurements based on the optimisation of a recursive least mean square error [17][18][19]. Thus, the accuracy of system outputs is improved.…”

Sliding mode corrector for jet UAV control

“…In order to reduce disturbances in position and attitude, the popular methods of GPS/INS based on KF (Kalman filter) or EKF (extended Kalman filter) are used for signal fusion to overcome the limits of individual measurements based on optimisation of a recursive least mean square error [18][19][20][21]. Thus, measurement accuracy is improved.…”

“…20, α * = 0.6, where, * = {x, y, z, θ , φ, ψ}. According to the properties and tests of engine and digital servos, we select the control law parametres:k p1 = 16, k p2 = 8, k a1 = 25, k a2 = 8.…”

Signal corrector and decoupling estimations for UAV control

“…In order to reduce large errors in position/attitude angle, KF (Kalman filter) or EKF (Extended Kalman filter) is adopted for signals integration/fusion to restrict the defects of individual measurements [17][18][19]. Thus, the accuracy of system outputs is improved.…”