The attitude estimation of three-axis stabilized satellites using hybrid particle swarm optimization combined with radar cross section precise prediction

Abstract

An attitude estimation approach based on the hybrid particle swarm optimization (HPSO) and radar cross section (RCS) precise prediction for the three-axis stabilized satellites in space is presented in this paper. The modified strategies with quantum behavior operator and cloud mutation based on the multi-phase particle swarm optimization are used to increase the diversity of particles, reduce the objective function evaluation times and improve the global searching ability. The simulated RCS sequences of three-axis stabilized satellites at the specified epoch are determined by the method combined the precise orbit and the RCS of satellites calculated by fast multipole method. The mean square error between measured RCS sequences and simulated RCS sequences is considered as the objective function. The attitude estimation problem is transferred to an optimization problem by minimizing the objective function with attitude angles, which is solved by HPSO. The operation procedure is shown clearly, and the set of measurement is analyzed. The numerical results of optimizing the test functions showing better behavior of the proposed algorithm method than standard particle swarm optimization in terms of accuracy are provided. Representative examples validate the accuracy and the anti-noise jamming ability of the proposed algorithm for attitude estimation of three-axis stabilized satellites.