This study aims to analyze four different telescope pointing correction models to verify the highest accuracy of the laser ranging telescope corrected by the back propagation (BP) neural network model optimized by the proposed genetic algorithm and Levenberg–Marquardt. In this process, first, the observation data of 95 stars are used to solve the coefficients of the four models, and then the pointing accuracy of the telescope corrected by those four models is verified by the detection results of 22 stars. The results indicate that the pointing accuracy of the telescope corrected by the three traditional pointing correction models, the mount model, the spherical harmonic function model, and the basic parameter model, reaches approximately 15 in. in the azimuth and ∼10 in. in the pitch; however, the BP neural network model optimized by the genetic algorithm and Levenberg–Marquardt has a pointing accuracy of 3.42 in. in the azimuth and 2.44 in. in the pitch. Finally, different space debris is detected by the telescope corrected by this model. The results show that the pointing accuracy of the telescope corrected by this model probably increases to nine times in the azimuth and three times in the pitch. The results of this study prove that the BP neural network model optimized by the genetic algorithm and Levenberg–Marquardt greatly increases the pointing accuracy of the telescope and thus significantly improves the success rate of space debris detection.This study aims to analyze four different telescope pointing correction models to verify the highest accuracy of the laser ranging telescope corrected by the back propagation (BP) neural network model optimized by the proposed genetic algorithm and Levenberg–Marquardt. In this process, first, the observation data of 95 stars are used to solve the coefficients of the four models, and then the pointing accuracy of the telescope corrected by those four models is verified by the detection results of 22 stars. The results indicate that the pointing accuracy of the telescope corrected by the three traditional pointing correction models, the mount model, the spherical harmonic function model, and the basic parameter model, reaches approximately 15 in. in the azimuth and ∼10 in. in the pitch; however, the BP neural network model optimized by the genetic algorithm and Levenberg–Marquardt has a pointing accuracy of 3.42 in. in the azimuth and 2.44 in. in the pitch. Finally, different space debris is detected by the...

中文翻译：

---

面向空间碎片的激光测距望远镜指向校正算法研究

本研究旨在分析四种不同的望远镜指向校正模型，以验证由所提出的遗传算法和 Levenberg-Marquardt 优化的反向传播 (BP) 神经网络模型校正的激光测距望远镜的最高精度。在此过程中，首先利用95颗星的观测数据求解4个模型的系数，然后通过22颗星的探测结果验证这4个模型校正后的望远镜指向精度。结果表明，经安装模型、球谐函数模型和基本参数模型三种传统指向校正模型校正的望远镜指向精度在方位角达到约15英寸，在方位角达到约10英寸。沥青; 然而，通过遗传算法和 Levenberg-Marquardt 优化的 BP 神经网络模型的指向精度为 3.42 in. 方位角和 2.44 in. 俯仰角。最后，通过该模型校正的望远镜探测到不同的空间碎片。结果表明，经该模型校正后的望远镜指向精度可能提高到方位角的九倍和俯仰角的三倍。本研究结果证明，遗传算法和Levenberg-Marquardt优化的BP神经网络模型大大提高了望远镜的指向精度，从而显着提高了空间碎片探测的成功率。本研究旨在分析四种不同的望远镜指向校正模型，以验证由所提出的遗传算法和 Levenberg-Marquardt 优化的反向传播 (BP) 神经网络模型校正的激光测距望远镜的最高精度。在此过程中，首先利用95颗星的观测数据求解4个模型的系数，然后通过22颗星的探测结果验证这4个模型校正后的望远镜指向精度。结果表明，经安装模型、球谐函数模型和基本参数模型三种传统指向校正模型校正的望远镜指向精度在方位角达到约15英寸，在方位角达到约10英寸。沥青; 然而，通过遗传算法和 Levenberg-Marquardt 优化的 BP 神经网络模型的指向精度为 3.42 in. 方位角和 2.44 in. 俯仰角。最后，不同的空间碎片被...