Self-Calibration for Star Sensors

Jingneng Fu, Ling Lin, Qiang Li
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Abstract

Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.
星形传感器的自校准
为了解决星体识别和在轨星体传感器固有参数确定过程中的鸡生蛋、蛋生鸡的问题,本研究提出了一种不依赖于星体识别的星体传感器在轨自校准方法。首先,根据图像帧间星对星际角的不变性推导出星传感器的自校准方程,对星对星际角的真实值没有任何要求。然后,定义了从星点到星空传感器光学系统中心的光路常数约束,以减少自校准中的偏差估计。最后,开发了一种比例非线性最小二乘法来求解自校准方程,从而加速迭代收敛。我们的模拟和分析结果表明,有约束条件的在轨自校准中焦距估计的偏差比无约束条件的在轨自校准小两个数量级。此外,研究还表明,使用比例非线性最小二乘法求解自校准方程时,可在 10 次迭代中实现收敛。利用该方法获得的校准本征参数可直接用于传统的星图识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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