Research On Attitude Determination And Quality Improvement Technology Of Surveying And Mapping Satellite

In the field of photogrammetry,improving the accuracy of surveying and mapping satellite attitude determination and ensuring the integrity and accuracy of the image attitude information at the time of photography are the prerequisites for achieving target positioning,especially uncontrolled target positioning.Based on the measurement data of star sensor and gyro,combined with kalman filter theory,this thesis studies the satellite attitude estimation algorithm based on star sensor and gyro to complete the goal of improving the accuracy and efficiency of the satellite attitude determination algorithm.The main work and innovations of this thesis are as follows:(1)Aiming at the non-linear problem of the attitude determination system,the principles of EKF,UKF,and SRUKF dealing with system non-linearity are analyzed,combined with attitude kinematics theory and sensor measurement model,and a combination of star sensor and gyro framework based on EKF and SRUKF is constructed.Aiming at the problem of poor EKF attitude determination accuracy and low calculation efficiency of SRUKF,a simplified SRUKF algorithm is proposed,and the attitude determination accuracy and time complexity of each algorithm are analyzed.Through simulation experiments with different precision star sensors and gyro data,we analyse the characteristics and advantages of each algorithm,the experimental results show that compared with the SRUKF algorithm,the simplified algorithm has an efficiency increase of about 35%,and the simplified algorithm has better average attitude determination accuracy and stability.(2)Aiming at the problem of low-frequency error in the measured value of the star sensor,the mechanism of low-frequency error is analyzed and the low-frequency error is modeled in the form of fourier series.The expanded state equation and measurement equation of kalman filter under colored noise conditions are studied.The EKF algorithm based on state expansion is designed and implemented,whose attitude and low-frequency error coefficients are estimated together.Using simulated posture data,the effect of the algorithm is verified and the influence of the extended dimensionality of different states on the result of the posture determination is discussed.(3)According to the observation characteristics of low frequency error,this paper proposes to use the state residuals of the filtering process to detect low-frequency errors.Aiming at the deficiencies of the extended dimension EKF,an iterative extended dimension EKF algorithm is proposed to detect and compensate the low-frequency error of the attitude.The simulation experiments show that for the scenes with low-frequency errors in the measurement data,the iterative expanded EKF joint attitude determination algorithm can achieve better attitude determination accuracy.(4)To further improve the attitude accuracy,this paper designs and implements a joint attitude fixing algorithm based on bidirectional EKF,which can mitigate the influence of bad filtering initial values and EKF linearization errors on the attitude fixing results.Based on the idea of bi-directional filtering and neural network error compensation,combining radial basis function,an improved bi-directional filtering algorithm is proposed.The algorithm is verified by using star sensor data and gyro simulation data under Gaussian noise and low frequency noise conditions,and the experiments confirm that the improved bidirectional filtering algorithm of RBF neural network can improve the accuracy of attitude determination.To achieve the goal of attitude determination and quality improvement of mapping satellites,this paper improves the existing algorithm from both speed and accuracy perspectives.The relevant experiments are carried out with attitude sensor data to prove the superiority of the algorithm in this paper.