Journal ArticleDOI
Kalman Filtering for Spacecraft Attitude Estimation
TLDR
In this article, the authors present a review of the methods of Kalman filtering in attitude estimation and their development over the last two decades, focusing on three-axis gyros and attitude sensors.Abstract:
HIS report reviews the methods of Kalman filtering in attitude estimation and their development over the last two decades. This review is not intended to be complete but is limited to algorithms suitable for spacecraft equipped with three-axis gyros as well as attitude sensors. These are the systems to which we feel that Kalman filtering is most ap- plicable. The Kalman filter uses a dynamical model for the time development of the system and a model of the sensor measurements to obtain the most accurate estimate possible of the system state using a linear estimator based on present and past measurements. It is, thus, ideally suited to both ground-based and on-board attitude determination. However, the applicability of the Kalman filtering technique rests on the availability of an accurate dynamical model. The dynamic equations for the spacecraft attitude pose many difficulties in the filter modeling. In particular, the external torques and the distribution of momentum internally due to the use of rotating or rastering instruments lead to significant uncertainties in the modeling. For autonomous spacecraft the use of inertial reference units as a model replacement permits the circumvention of these problems. In this representation the angular velocity of the spacecraft is obtained from the gyro data. The kinematic equations are used to obtain the attitude state and this is augmented by means of additional state-vector components for the gyro biases. Thus, gyro data are not treated as observations and the gyro noise appears as state noise rather than as observation noise. It is theoretically possible that a spacecraft is three-axis stabilized with such rigidity that the time development of the system can be described accurately without gyro information, or that it is one-axis stabilized so that only a single gyro is needed to provide information on the time history of the system. The modification of the algorithms presented here in order to apply to those cases is slight. However, this is of little practical importance because a control system capable of suchread more
Citations
More filters
Journal ArticleDOI
Nonlinear Complementary Filters on the Special Orthogonal Group
TL;DR: An observer on SO(3), termed the explicit complementary filter, that requires only accelerometer and gyro outputs; is suitable for implementation on embedded hardware; and provides good attitude estimates as well as estimating the gyro biases online.
Journal ArticleDOI
Survey of nonlinear attitude estimation methods
TL;DR: A survey of modern nonlinear filtering methods for attitude estimation based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance is provided.
Journal ArticleDOI
Unscented Filtering for Spacecraft Attitude Estimation
TL;DR: In this paper, an unscented filter is used to estimate the attitude of a spacecraft in the presence of a gyro-based model for attitude propagation, and a multiplicative quaternion-error is derived from the local attitude error, which guarantees that quaternions normalization is maintained in the filter.
Journal ArticleDOI
Attitude Error Representations for Kalman Filtering
TL;DR: In this paper, the authors consider various attitude error representations for the Multiplicative Extended Kalman Filter (MEFL) and its second-order extension, and compare them with a three-component representation for attitude errors.
Journal ArticleDOI
Autonomous Helicopter Aerobatics through Apprenticeship Learning
TL;DR: These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics, including the first autonomous execution of a wide range of maneuvers, including in-place flips, in- place rolls, loops and hurricanes.
References
More filters
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
New Results in Linear Filtering and Prediction Theory
R. E. Kalman,R. S. Bucy +1 more
TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.