Kalman Filtering for Spacecraft Attitude Estimation
Фильтрация Калмана для оценки ориентации космического аппарата
1982-09-01
SCID: 54.1/25eeum5p
Discuss with AI
Kalman filteringattitude determinationgyro bias estimationspacecraft attitude estimationthree-axis gyros
Figures from the paper
Abstract (AI)
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 such
Key Findings
1
Accurate dynamical modeling is a central limitation because external torques and internally redistributed momentum create substantial modeling uncertainties.
2
In this inertial-reference representation, gyro noise is modeled as state noise rather than observation noise, and the algorithms can be adapted to systems requiring fewer than three gyros under suitable stabilization conditions.
3
Kalman filtering combines spacecraft dynamical and sensor-measurement models to estimate attitude from present and past measurements, supporting both ground-based and onboard determination.
4
The report reviews Kalman-filtering methods for spacecraft attitude estimation using three-axis gyros together with attitude sensors.
5
Using inertial reference units as a model replacement avoids difficult spacecraft-dynamics modeling by propagating attitude from gyro-derived angular velocity and estimating gyro biases as additional states.
Research Object
spacecraft attitude-determination systems equipped with three-axis gyros and attitude sensors
Research Subject
Kalman-filtering methods and their applicability for estimating spacecraft attitude and gyro biases under dynamical-model and torque uncertainties
Publication Details
Publication Date
1982-09-01
Journal
Publisher
ISSN
Access Type
Author Information
Download PDF
Subscribe to digest