Research on pointing correction algorithm of laser ranging telescope oriented to space debris

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.