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考虑传统的星敏感器标定方法忽略了星敏感器的畸变与光学参数之间的相互作用而引入的额外误差,提出了一种基于非线性优化的星敏感器自主标定算法。该算法首先忽略星敏感器畸变的影响,构建目标函数,利用LevenbergMarquardt非线性优化算法优化星敏感器的光学参数;然后,将得到的光学参数估计值作为理想值,通过线性最小二乘法估计相机的镜头畸变系数;最后,将前两个步骤获得的参数作为初始值,构建目标函数,利用Levenberg-Marquardt算法同时优化光学参数和畸变系数。开展了仿真实验研究,并与最小二乘法和Samman法的标定结果做了对比,结果表明:提出的方法能够很好地实现星敏感器的自主标定。在同等测试条件下,文中算法获得的最大残差为0.015pixels,精度高于其它两种标定方法两个数量级。星敏感器外场实验还表明,提出的优化方法有效提升了星敏感器的性能。
Considering that the traditional star sensor calibration method ignores the extra error introduced by the interaction between the star sensor distortion and the optical parameters, a non-linear optimization star sensor autonomic calibration algorithm is proposed. The algorithm firstly ignores the influence of star sensor distortion and constructs the objective function. The Levenberg-Marquardt nonlinear optimization algorithm is used to optimize the optical parameters of the star sensor. Then, the estimated optical parameter is regarded as the ideal value, and the linear least squares method is used to estimate the camera’s Lens distortion coefficient. Finally, the parameters obtained in the first two steps are taken as the initial values to construct the objective function. The Levenberg-Marquardt algorithm is used to optimize both the optical parameters and the distortion coefficients. The simulation experiment is carried out and compared with the least squares method and the calibration results of Samman method. The results show that the proposed method can realize the autonomic calibration of the star sensor. Under the same test conditions, the maximum residual error obtained by the algorithm in this paper is 0.015pixels, which is two orders of magnitude higher than the other two calibration methods. Star sensor field experiments also show that the proposed optimization method effectively enhance the performance of the star sensor.