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A Novel Systematic Error Compensation Algorithm Based on Least Squares Support Vector Regression for Star Sensor Image Centroid Estimation

机译：基于最小二乘支持向量回归的恒星传感器图像质心估计新系统误差补偿算法

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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.
机译：星形质心估计是最重要的操作，它直接影响星形传感器姿态确定的精度。本文对星心估计算法引入的系统误差进行了理论研究。通过频域方法和数值模拟分析了系统误差。结果表明，系统误差由近似误差和截断误差组成，它们分别由离散化近似和采样窗口限制引起。本文提出了一种选择采样窗口大小以减少截断误差的准则。系统误差可以根据恒星分布不同高斯宽度下实际恒星质心位置的函数进行评估。为了消除系统误差，提出了一种基于最小二乘支持向量回归（LSSVR）和径向基函数（RBF）核的补偿算法。仿真结果表明，将补偿算法应用于5像素恒星采样窗口，可以将恒星质心估计的精度从0.06提高到6×10 -5 像素。

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