Research On Star Centroid Algorithm Of Star Sensor

Star sensor is an independent attitude-sensitive measuring instrument.Because of its high precision and high reliability,it is widely used in aerospace fields.The centroid algorithm of the star sensor is a key part of the star sensor.Improving the accuracy of the centroid algorithm is very important for the attitude accuracy of the star sensor.In this thesis,the methods to improve the accuracy of star centroid are studied and the simulation results verify the effectiveness of the presented algorithms.In this thesis,the development and research status of star sensor centroid algorithms are introduced.The working principle and star point energy model of star sensor are introduced.The main factors leading to centroid calculation error are analyzed,including image sensor noise,saturation error,negative bias error,fill factor error,and aberration,etc.,and the characteristics and models of different noise are also discussed.Secondly,the commonly used centroid algorithm is introduced in detail,including centroid method and Gaussian fitting method.The systematic error of the algorithm is analyzed.On this basis,an improved method of centroid calculation for error compensation is proposed.One is to compensate the systematic error of the centroid method by numerical method,and the other is to use the nonlinear Gaussian fitting to carry out error compensation and centroid calculation on the basis of the error data.The nonlinear Gaussian fitting,log-Gaussian fitting and integral nonlinear fitting are discussed respectively,and the error of the centroid algorithm for Gaussian fitting is analyzed.The theoretical analysis and simulation results show that the proposed algorithms can effectively reduce the systematic error and improve the accuracy of centroid.Thirdly,aiming at the influence of background noise on the accuracy of centroid,a method of suppressing background noise is proposed.On the basis of the traditional background prediction algorithm,a mean-median background prediction method for the whole star maps is proposed.In order to further improve the SNR of the star map data,a background noise filtering method based Wiener filter on for local star map is proposed.The simulation results show that the proposed method can effectively suppress the background noise on the star image and improve the centroid accuracy.Finally,an algorithm for estimating motion blur of star sensor is proposed for motion-induced star image distortion.The method focuses on the energy distribution of the star point,uses the nonlinear Gaussian fitting to obtain the Gaussian parameters,and to estimate motion parameters.Compared with the traditional method,this algorithm can extract the star point and determine the motion parameters as well,then through motion compensation to improve the accuracy of centroid.For the proposed algorithms,simulation experiments are designed and implemented.The results show that the proposed algorithms can effectively improve the accuracy of the centroid,increase the speed,and meet the requirements of the star sensor for attitude update rate.