Unscented Kalman filtering (UKF) based combined attitude determining method and satellite attitude control system

Abstract

The invention discloses a UKF based combined attitude determining method and satellite attitude control system, and belongs to the technical field of satellite attitude determining. A nonlinear stateequation in the differential form is constructed based on a star sensor of UKF and a gyro combined pose determining algorithm, a four-order Runge-Kutta integration method is used to solve time updateof a state variable, and discretization of the nonlinear differential state equation is avoided; and the UKF algorithm is used to filter and correct a quaternion of the star sensor and the gyro angular speed. The four-order Runge-Kutta integration method is used to update the time, a quaternion multiplication error is used to calculate the weighted mean value and covariance, the UKF algorithm is used, an observed value of the star sensor is introduced to implement filtering and updating, and the estimated error quaternion is used to correct attitude data. The attitude determining performance when a measurement error of an attitude sensor is relatively large is high, and compared with an attitude determining algorithm based on expanded Kalman filtering, the estimation precision is higher.

Description

technical field [0001] The invention belongs to the technical field of satellite attitude determination, in particular to a joint attitude determination method based on an unscented Kalman filter and a satellite attitude control system. Background technique [0002] At present, the closest existing technology: attitude determination technology is the process of processing the measured value of the attitude sensor with error through a certain algorithm, reducing the error, and correcting the measured value. It is widely used in satellite navigation and positioning and other fields. For surveying and mapping satellites, the accuracy of attitude determination is an important prerequisite for high-precision positioning. At present, satellites mainly use the combination of star sensor and gyroscope, and use dynamic estimation method for filtering. The dynamic estimation method uses a series of continuous time measurement information, adopts attitude kinematics models and other t...

AI Technical Summary

Problems solved by technology

However, when the UKF algorithm solves the nonlinear attitude determination problem where the state equation is a continuous nonlinear ordinary differential equation system, it usually discretizes the state equation. On the one hand, the derivation of the discretization process is complicated, and on the other hand, discretization errors will occur.

In addition, when the traditional UKF solves the mean and covariance of the state variables, the quaternion is equivalent to the general vector solution, which cannot meet the normalization condition of the quaternion

[0003] To sum up, the problems existing in the existing technology are: the existing algorithm linearizes and discretizes the state equation and the measurement equation, which inevitably produces linearization and discretization errors, and ignores the quaternion operation characteristics and Physical meaning, leading to filter performance degradation

[0005] The difficulty of the above technical problems lies in: 1) how to avoid linearization and discretization processing, and effectively improve the accuracy of attitude estimation; 2) how to apply the operational characteristics of quaternions to the process of solving the mean value and covariance matrix

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