| 其他摘要 | Fine guidance sensor is a high-precision measurement of large-aperture space telescope image stabilization important part of the control system , is the most precise angular position of the measuring element. It uses the main optical system is extremely long focal length correlation algorithm to obtain highly accurate inertial angular position offset information in a way to fine -level image stabilization control system provides feedback information to achieve fine control level stabilization loop control system . In the case of the primary optical system, the focal length of the telescope determined , star point positioning accuracy subdivision directly determine the angular position measurement accuracy inertial measurement guide star system . In this paper as a background , the main research guide star measurement system suitable for subdivision positioning technology . CMOS detectors for low fill factor, affecting the shape of the photosensitive area subdivision positioning accuracy is proposed based on least squares support vector machine algorithm for error correction system . Analysis of statistical learning theory , detailing least squares support vector machine regression mathematical model , then as a theoretical basis for the establishment of the star point positioning system error correction model , and finally, the numerical simulation using Monte Carlo method, with Gaussian radial basis function kernel least squares support vector machine regression analysis, the non-linear function of system errors and centroid location and the ideal size and shape of the light-sensitive area , and use this as a function of the heart after a confrontation estimates corrected.Tracking for high dynamic conditions and guide star problems CMOS detector can play any windows features , is proposed based on adaptive Kalman filtering sub-pixel subdivision algorithm . First study the Kalman filter theory, the star point positioning method is proposed to predict window and using a combination of Kalman filtering . Coarse location prediction method uses the gyro output window INS information is star point star point in the measurement position predicted by the smaller star point range to determine the position of the window , and then , using the Kalman filter for filtering coarse location , high output Star point accuracy . As the star point measurement position calculation in a small window , while the Kalman filter can effectively suppress random noise, is particularly suitable for measuring guide star system of such high magnitude star point low SNR image , can provide highly accurate satellite position information . Low -noise ratio for high- magnitude image and the image shift and errands like oval spot problems , the use of statistical signal processing theory first spot signal modeling to derive maximum likelihood estimation formula spot center.Then proposed an iterative weighted centroid calculation processes, statistical signal processing theory to derive the Cramer-Rao lower limit when the number of iterations to infinity, the star point positioning accuracy limits . When iterative weighted centroid algorithm converges , through numerical simulation results show that the proposed algorithm has good performance. Build a ground test measuring guide star system, the proposed algorithm for authentication. First introduced the composition and working principle experiment system ; experiment parameters related devices for analysis ; compensated by the system error experiments , static and dynamic accuracy of the measurement accuracy of the experimental measurement experiments conducted experiments to verify the proposed algorithm achieved the expected experimental results . Experimental results show that, under operating conditions guide star measurement system , the measurement accuracy of the proposed method compared to the conventional positioning subdivision algorithm greatly improved. Research results have certain significance for the development of China's space telescope guide star precision measurement system . |
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