Spacecraft angular rate estimation from magnetometer data only

A method is presented for fast estimation of the angular rate of a tumbling spacecraft in a low-Earth orbit, from sequential readings of Earth's magnetic field. Useful as a backup algorithm in cases of rate gyro malfunctions, or during the initial acquisition phase, the estimator consists of an extended Kalman filter, based on the underlying assumption that the geomagnetic field vector does not significantly change (relative to an inertial frame of reference) during the short sampling time. Contrary to previously introduced angular rate estimators, the spacecraft's attitude is not assumed to be known (nor is it estimated as part of the proposed procedure). Moreover, the body-referenced geomagnetic field observations are not differentiated with respect to time as a prefiltering procedure, but are directly processed by the filter. A simulation study employing the standard 8th order IGRF geomagnetic field model is presented to demonstrate the performance of the algorithm. © 2000 by P. Tortora and Y. Oshman. Published by the American Institute of Aeronautics and Astronautics, Inc

Attitude independent estimation of spacecraft angular rate using geomagnetic field observations

A method is presented for fast estimation of the angular rate of a tumbling spacecraft in a low-Earth orbit, fiom sequential readings of Earth's magnetic field. Useful as a backup algorithm in cases of rate gyro malfunctions, or during the initial acquisition phase, the estimator consists of an extended Kalman filter, based on the underlying assumption that the geomagnetic field vector does not significantly change (relative to an inertial fiame of reference) during the short sampling time. Neglecting the external disturbance torque, the analytical solution of the rigid body motion in terms of the Jacobian elliptic functions can be used in the propagation phase of the filter. This strategy allows a significant saving in terms of computation time, compared to numerical integration of the Euler's equations between two sampling times. Contrary to previously introduced angular rate estimators, the spacecraft's attitude is not assumed to be known (nor is it estimated as part of the proposed procedure). Moreover, the body-referenced geomagnetic field observations are not differentiated with respect to time as a prefiltering procedure, but are directly processed by the filter. A simulation study employing a standard 10th order IGRF geomagnetic field model is presented to demonstrate thc performance of the algorithm. © 2003 IEEE

Spacecraft Angular Rate Estimation from Magnetometer Data Only Using an Analytic Predictor

A method is presented for fast estimation of the angular rate of a tumbling spacecraft in a low-Earth orbit from sequential readings of Earth\u2019s magnetic field. Useful as a back up algorithm in cases of rate gyro malfunctions or during the initial acquisition phase, the estimator consists of an extended Kalman filter, based on the assumption that the inertial geomagnetic field vector does not significantly change during the short sampling time. As the external disturbance torque is neglected, an analytic solution of Euler\u2019s equations can be used in the filter\u2019s propagation phase, allowing a significant savings of computation time compared to numerical integration of Euler\u2019s equations. Contrary to most existing angular rate estimators, the spacecraft\u2019s attitude is neither used nor estimated within the proposed algorithm. Moreover, the body-referenced geomagnetic field observations are not differentiated with respect to time as an external prefiltering procedure but are directly processed by the filter. This processing gives rise to a colored effective measurement noise,which is properly handled via approximate Markov modeling and application of Bryson and Henrikson\u2019s reduced-order filtering theory. A simulation study employing a standard tenth-order International Geomagnetic Reference Field model is presented to demonstrate the performance of the algorithm

Novel quaternion Kalman filter

This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quaternion kinematics equation is linear, the combination of the two yields a linear KF that eliminates the usual linearization procedure and is less sensitive to initial estimation errors. General accurate expressions for the covariance matrices of the system state-dependent noises are developed. In addition, an analysis shows how to compute these covariance matrices efficiently. An adaptive version of the filter is also developed to handle modeling errors of the dynamic system noise statistics. Monte-Carlo simulations are carried out that demonstrate the efficiency of both versions of the filter. In the particular case of high initial estimation errors, a typical extended Kalman filter (EKF) fails to converge whereas the proposed filter succeeds

Guest Editor's Note/Nota de la Editora Invitada

This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quaternion kinematics equation is linear, the combination of the two yields a linear KF that eliminates the usual linearization procedure and is less sensitive to initial estimation errors. General accurate expressions for the covariance matrices of the system state-dependent noises are developed. In addition, an analysis shows how to compute these covariance matrices efficiently. An adaptive version of the filter is also developed to handle modeling errors of the dynamic system noise statistics. Monte-Carlo simulations are carried out that demonstrate the efficiency of both versions of the filter. In the particular case of high initial estimation errors, a typical extended Kalman filter (EKF) fails to converge whereas the proposed filter succeeds