Research On Satellite Attitude Determination Algorithms Based On Star Sensor And Gyro Combination

Satellite attitude determination is an important part of the whole satellite attitude control system.The classical attitude determination algorithm does not consider the problem that the error vector estimation is inaccurate due to the deviation between the coordinate systems.At the same time,there is some improvement in the filtering performance of the system model under the uncertainty disturbance and the abnormal measurement value.In this paper,taking the star sensor and gyro combination of attitude determination system as the research object,in response to the aforementioned problems to improve the corresponding algorithm respectively,the main research content consists of the following parts:The second chapter introduces the definition of the attitude coordinate system,the attitude parameter and the conversion formula.The kinematic equations of attitude are given under different attitude parameters.The results show that the precision of several static algorithms is not different from each other.The significant difference between them is the difference of the running time.Euler-q method is used to determine the attitude of the static deterministic algorithm than other algorithms in the operating efficiency has certain advantages.In the third chapter,MEKF(Multiplicative Extended Kalman Filter)algorithm does not consider the difference between the estimated coordinate system and the real coordinate system,and the error vector is inconsistent.MEKF algorithm is improved by geometric transformation.Simulation results show that the improved algorithm is not affected by the initial attitude error,and the filtering accuracy is better than MEKF algorithm.In chapter 4,an adaptive Kalman/H_∞ fusion filter algorithm is proposed and applied to the satellite attitude determination for the problem that the model is affected by uncertain perturbation and Kalman filter and H_∞ filter can not take into account robustness and accuracy.The algorithm calculates the performance index J of Kalman filter in real time.By assigning the weight between Kalman filter and H_∞ filter,the final filtered output will be the weighted sum of the two filtering algorithms.The simulation results show that the fusion algorithm can obtain good effect in accuracy and robustness.In Chapter 5,only one of Kalman filtering and strong tracking filtering algorithms is used to solve the problem of anomaly measurement.The adaptive strong tracking filter is applied to satellite Attitude determination.The performance of Kalman filter is calculated in real time,and the binary hypothesis test is adopted as the switching criterion between Kalman filter and strong tracking filter.The simulation results show that the adaptive algorithm can take into account the accuracy and robustness of the sensor when the measured value is abnormal.The results of this paper can provide some new ideas for satellite attitude determination and provide theoretical reference for practical engineering application...