The integrated Singular Value Decomposition (SVD) and Unscented Kalman Filter (UKF) method can recursively estimate the attitude and attitude rates of a nanosatellite. At first, Wahba’s loss function is minimized using the SVD and the optimal attitude angles are determined on the basis of the magnetometer and Sun sensor measurements. Then, the UKF makes use of the SVD’s attitude estimates as measurement results and provides more accurate attitude information as well as the attitude rate estimates. The elements of “Rotation angle error covariance matrix” calculated for the SVD estimations are used in the UKF as the measurement noise covariance values. The algorithm is compared with the SVD and UKF only methods for estimating the attitude from vector measurements. Possible algorithm switching ideas are discussed especially for the eclipse period, when the Sun sensor measurements are not available.
Conventionally, the filtering technique for attitude estimation is performed using gyros or attitude dynamics models. In order to extend the application range of an attitude filter, this paper proposes a quaternionbased filtering framework for gyroless attitude estimation without an attitude dynamics model. The attitude estimation system is established based on a quaternion kinematic equation and vector observation models. The angular velocity in the system is determined through observation vectors from attitude sensors and the statistical properties of the angular velocity error are analysed. A Kalman filter is applied to estimate the attitude error such that the effect from the angular velocity error is compensated with its statistical properties at each sampling moment. A numerical simulation example is presented to illustrate the performance of the proposed algorithm.
Estimation of satellite three-axis attitude using only one sensor data presents an interesting estimation problem. A flexible and mathematically effective filter for solving the satellite three-axis attitude estimation problem using two-axis magnetometer would be a challenging option for space missions which are suffering from other attitude sensors failure. Mostly, magnetometers are employed with other attitude sensors to resolve attitude estimation. However, by designing a computationally efficient discrete Kalman filter, full attitude estimation can profit by only two-axis magnetometer observations. The method suggested solves the problem of satellite attitude estimation using linear Kalman filter (LKF). Firstly, all models are generated and then the designed scenario is developed and evaluated with simulation results. The filter can achieve 10e-3 degree attitude accuracy or better on all three axes.