Abstract
The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels.
Highlights
The star tracker is a satellite-based embedded system which estimates the orientation of the satellite in space
We design a number of experiments to verify the performance of the systematic error compensation algorithm based on the least square support vector regression
The sampling frequency limitation and sampling window size limitation are fully considered and the systematic error is divided into an approximation error and a truncation error
Summary
The star tracker is a satellite-based embedded system which estimates the orientation of the satellite in space. Systematic error of centroid estimation is related with the energy distribution of starlight on star image (Gaussian width), the frequency of sampling, the size of sampling window and the actual position of star point. JIA et al [10] studied the systematic error utilizing a frequency domain method considering sampling frequency limitation and sampling window limitation He proposed an analytical compensation algorithm to reduce the systematic error of star centroid estimation. In order to eliminate the systematic error, a novel systematic error compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed.
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