Spacecraft posture confirming method based on Euler-q algorithm and DD2 filtering

Abstract

A spacecraft attitude fixing method of DD2 (Divided Difierence 2) filter and a counting method based on Euler-q (Euler-quaternion) are provided, which relates to a state estimation and attitude determination method based on vector observation. The invention is characterized in that: according to the reference ephemeris and the reference vector and the observation vector observed by a star sensor, the spacecraft attitude quaternion can be gotten through Euler-q counting method; through DD2 filter, on one hand, feedback compensation the attitude matrix obtained through resolving by gyro; on the other hand, revise the palstance output of three-axle gyro and conduct compensation to the constant drift of gyroscope. As for that, the current spacecraft attitude information when getting high precision can be achieved, the constant drift of gyroscope can be scaled on line. The invention is an unattended attitude determining method, which is characterized in high precision and good timeliness, being convenient and feasible, and can be used in the attitude fixing system of various spacecrafts.

Description

technical field [0001] The present invention relates to a method for determining the attitude of a spacecraft, especially a method for determining the attitude of a spacecraft based on Euler-q (Euler-quaternion) algorithm and DD2 (Divided Difference 2) filtering, which is used for various medium and high precision inertial / Attitude determination for astronomical integrated navigation systems. Background technique [0002] The attitude determination task of the spacecraft is to use the information measured by the attitude sensor on the spacecraft to obtain the information fixed in a certain reference coordinate system relative to the space of the spacecraft body coordinate system after appropriate processing. The requirements of this mission require high-precision attitude information, which is one of the key technologies for the development and wide application of spacecraft. [0003] Inertial / astronomical, that is, INS (Inertial Navigation System) / CNS (Celestial Navigatio...

AI Technical Summary

Problems solved by technology

At present, most spacecraft attitude determination models use the attitude quaternion as the state. Since the degree of freedom of the three-axis attitude is 3, there is redundancy in the attitude quaternion. If the vector of the attitude quaternion is simply taken as the estimated state, the estimation model must be linearized and simplified, resulting in a reduction in the accuracy of the model

Applied to spacecraft attitude determination methods, purely using deterministic vector observations, for example, the Davenport q method needs to calculate eigenvalues ​​and eigenmatrices, and online calculation is difficult, so it is difficult to be applied

The improved QuEsT (Quaternion Estimator) method based on the Davenport q method has been greatly improved in the algorithm, but it still has the disadvantages of relying too much on the accuracy of the star sensor and cannot correct the gyro drift and output the real-time attitude.

SVD (the Singular ValueDecomposition) is very robust but requires singular value decomposition. Since singular value decomposition is a complex work with a large amount of calculations, it is not very effective

The FOAM (A Fast Optimal Matrix Algurithm) algorithm cannot estimate the gyro drift so that the gyro output can be calibrated online; while using other state estimation methods, such as the Extended Kalman Filter EKF (Extend Kalman Filter), due to the first-order nonlinear processing using Taylor expansion , although the nonlinear model can be estimated, but the estimation accuracy of the nonlinear model is not high, and the UKF (Unsented Kalman Filter) uses a series of sampling points approximately Gaussian distribution, and performs the recursive sum of the state and error covariance through the Unscented transformation Update, although the accuracy has been improved, but the calculation is cumbersome and the real-time performance is not high, so it is difficult to meet the real-time requirements of attitude determination

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