Abstract
In order to realize the high precision attitude determination of the spacecrafts by star sensors, it is necessary to optimize accurately and rapidly the modeling of distorted star map. For the model, there are some shortcomings in traditional optimization methods, which result in large errors, slow speed and apt to falling into local optimal, etc. In this paper, an optimization method of star map distorted model based on improved genetic algorithm is presented. Different from the previous genetic algorithm, the coded-decimal notation is adopted to increase the execute-speed and real-time performance, the fitness evaluation function is improved to avoid premature convergence of genetic algorithms and the cross and mutation probability is selected appropriately to optimize model of distortion. Semi-physics simulation results show that compared with the traditional genetic algorithm, this method not only improves the speed but also greatly enhances the accuracy of the model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.