Norm constraint strong tracking cubature kalman filter method for satellite attitude estimation

Abstract

The invention discloses a norm constraint strong tracking cubature kalman filter method for satellite attitude estimation. The norm constraint strong tracking cubature kalman filter method comprises the following steps: 1, acquiring output data of a gyro and a star sensor; 2, determining state variables and measuration amount of a satellite attitude estimation system; 3, performing cubature kalman filter time updating and measuration updating at the moment of k to obtain one-step state prediction variance, one-step measuration prediction variance and cross covariance; 4, using a multiple gradual-fading factor for correction of the one-step prediction variance; 5, performing cubature kalman filter measuration updating again, acquiring state variance and state estimation variance at the moment of k +1; 6 if K + 1 = M (M is end moment of an attitude estimation nonlinear discrete system), outputting attitude quaternion and gyroscopic drift of state estimation of the moment of k +1 to complete the attitude estimation, if K + 1 < M, and k = K + 1, repeating steps 3, 4,and 5. The a norm constraint strong tracking cubature kalman filter method has the advantages of high estimation accuracy and strong robustness.

Description

technical field [0001] The invention belongs to the technical field of satellite attitude estimation, in particular to a norm-constrained strong tracking volumetric Kalman filter method for satellite attitude estimation. Background technique [0002] Satellite attitude estimation technology is one of the key technologies of aerospace technology. The satellite attitude estimation system composed of gyroscope and star sensor has been widely used due to its advantages of high attitude measurement accuracy, good reliability and strong autonomy. For the attitude estimation system, the quaternion is used as the attitude description parameter of the system because of its simple calculation, no trigonometric operation, and avoiding the singularity of Euler angles. In order to improve the accuracy of attitude estimation and the adaptability and robustness of the attitude estimation system, the nonlinear filtering algorithm provides a strong basic guarantee. Extended Kalman Filter (E...

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Problems solved by technology

However, EKF has theoretical limitations: 1) model linearization introduces truncation errors, resulting in a decrease in filtering accuracy, and the calculation of the Jacobian matrix is ​​complicated; 2) in the case of model mismatch, unknown interference or state mutation, etc., Poor robustness; 3) Quaternions have norm constraints as state variables, which affect filtering accuracy

Since then, some scholars have combined the strong tracking idea with the CKF algorithm and proposed the strong tracking CKF (STCKF) algorithm, but this algorithm only adjusts the covariance matrix of the prediction error by introducing a single-order fading factor, although it has good tracking ability , but for complex multi-variable systems, it is impossible to guarantee a good tracking ability for each variable, and it does not consider the constraints of state variables

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