Initial Attitude Acquisition of Star Sensor based on NSGA2-LM Hybrid Optimization Algorithms

Abstract

For the initial attitude acquisition problem of star sensor, a new method based on optimization is proposed in this paper, which can obtain accurate attitude information of the star sensor when necessary information is missing. Firstly, a set of overdetermined non-linear equations for solving the line of sight (LOS) of star sensor is established by using the collinear equation, and the optimal model for solving the equations is established according to the minimum sum of squared errors (SSE). On this basis, the multi-objective optimization models for attitude determination of the star sensor are established respectively when the direction information of star in the image plane and the focal length information of the star sensor is missing. When solving the above models, a hybrid optimization algorithm based on NSGA2-LM is adopted. This method uses Non-dominated Sorting Genetic Algorithm 2 (NSGA2) with strong global search ability to obtain the Pareto frontier of star sensor's LOS. Then, an optimal solution is selected from Pareto optimal solution set as the initial value of Levenberg-Marquardt (LM) algorithm, and the local optimal solution of LOS is obtained by using LM algorithm with strong local search ability. Finally, the validity of the proposed method is verified by the experimental data in [1].