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Sensors 2011, 11(8), 7341-7363; doi:10.3390/s110807341
Article

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

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Received: 13 May 2011; in revised form: 18 July 2011 / Accepted: 22 July 2011 / Published: 25 July 2011
(This article belongs to the Section Physical Sensors)
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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.
Keywords: star sensor; subpixel; centroid estimation; systematic error compensation; LSSVR star sensor; subpixel; centroid estimation; systematic error compensation; LSSVR
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Yang, J.; Liang, B.; Zhang, T.; Song, J. A Novel Systematic Error Compensation Algorithm Based on Least Squares Support Vector Regression for Star Sensor Image Centroid Estimation. Sensors 2011, 11, 7341-7363.

AMA Style

Yang J, Liang B, Zhang T, Song J. A Novel Systematic Error Compensation Algorithm Based on Least Squares Support Vector Regression for Star Sensor Image Centroid Estimation. Sensors. 2011; 11(8):7341-7363.

Chicago/Turabian Style

Yang, Jun; Liang, Bin; Zhang, Tao; Song, Jingyan. 2011. "A Novel Systematic Error Compensation Algorithm Based on Least Squares Support Vector Regression for Star Sensor Image Centroid Estimation." Sensors 11, no. 8: 7341-7363.



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