A method of optimization for the distorted model of star map based on improved genetic algorithm

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.