Wu Liang, Xu Qian, Heikkilä Janne, Zhao Zijun, Liu Liwei, Niu And Yali
Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland.
Sensors (Basel). 2019 Jul 26;19(15):3301. doi: 10.3390/s19153301.
The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.
星敏感器的导航精度取决于其光学参数的估计精度,因此,应实时更新这些参数以获得最佳性能。目前星敏感器的在轨标定方法主要依赖于恒星之间的角距,很少有研究致力于寻找新的标定参考。本文介绍并研究了一种以奇异值作为标定参考的在轨标定方法。首先,给出了星敏感器的相机模型。然后,基于奇异值在坐标变换下的不变性,提出了一种基于奇异值分解(SVD)方法的在轨标定方法。通过可观性分析,探索了用于标定的恒星组合的最优模型。根据星矢量矩阵奇异值分解的物理解释,讨论了用于标定的奇异值选择。最后,为了验证SVD方法的性能,分别采用SVD方法和传统的基于角距的方法进行了仿真标定。结果表明,两种方法的精度和收敛速度相似;然而,SVD方法的计算成本大幅降低。此外,还进行了现场实验以验证SVD方法的可行性。因此,SVD方法在星敏感器的标定中表现良好,尤其适用于计算资源有限的星敏感器。