Satellite sensor gyroscope combined attitude determination method, combined attitude determination system and application

Abstract

The invention belongs to the technical field of satellite attitude determination, and discloses a satellite sensor and gyroscope combined attitude determination method, a combined attitude determination system and application, and the method comprises the following steps: calculating a satellite attitude at a current moment k through an attitude estimation result of filtering at a k-1 moment and a satellite angular velocity measured by a gyroscope; performing unscented transformation on the mean value of the satellite attitude errors to obtain a sampling point; constructing a nonlinear system equation, performing nonlinear state updating on the sampling point, and performing cholesky updating to obtain a state prediction mean value and a variance square root; constructing a linear measurement equation, and performing linear measurement updating according to the predicted attitude error mean value; using Kalman filtering to fuse observation information and a predicted value to estimate an attitude error; and compensating the attitude estimation result by using the attitude error. According to the method, linear measurement updating is used for replacing nonlinear measurement updating of the system in combination with an application scene determined by the attitude, so that the calculation amount of the SRUKF is reduced, and the filtering efficiency can be improved.

Description

Technical field [0001] The present invention belongs to the technical field of satellite attitude, and more particularly to a star-sensor gyro combined graceration method, a combined grace system and application. Background technique [0002] At present, the gesture measurement system is an important part of the high-resolution satellite project. Attitude measurement data is not only used for satellite attitude control, but also converted to the external angle element required for photogrammetry processing, applied to high-precision satellite images. Positioning scenes. The posture determination algorithm based on each posture sensitive sensor information has a high application value for posture control and no control point photographic measurement. [0003] The stratigratic algorithm is mainly divided into two types of determining a degree of stratigrating algorithm and state estimation calculation. Determining the basic idea of ​​nature is based on the observing reference vecto...

AI Technical Summary

Problems solved by technology

[0008] (1) In the measurement of the existing attitude determination system, high-precision attitude information cannot be calculated in a short period of time, and at the same time, it cannot show better adaptability to the error change of the star sensor. Speed ​​and Accuracy Requirements

[0009] (2) In the measurement of the attitude determination system in the existing technology, the algorithm based on EKF is simple to implement, but it needs to linearize the system model, which is only suitable for weak nonlinear systems, the filtering stability is poor, and the accuracy is limited by the Jacobian matrix Calculations have a greater impact

[0010] (3) In the measurement of the existing technology attitude determination system, the algorithm based on UKF does not perform linear approximation, which can improve the accuracy. It is often necessary to use the integral method to perform nonlinear state update and measurement update for each sampling point, and the algorithm efficiency is relatively low. Low

[0011] (4) In the measurement of the existing technology attitude determination system, the deterministic attitude determination algorithm (such as two-vector attitude determination, etc.) completely depends on the accuracy of the observation sensor, so it is seriously affected by the observation noise, and must have observations at every moment value to apply deterministic attitude determination algorithm, it is difficult to adapt to the scene of satellite attitude estimation under high precision requirements

[0012] The difficulty of solving the above problems and defects is: it is difficult to balance the efficiency and accuracy of the algorithm to improve the algorithm under the framework of EKF or UKF alone

The EKF linearization step will inevitably lead to a loss of accuracy while improving the computational efficiency, while the discrete sampling step of the UKF will increase the amount of calculation of the system. Theoretically, with the increase in the number of sampling points, the accuracy of the algorithm will gradually improve. , but updating each sampling point will bring a computational burden to the system

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