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针对星敏感器光学误差两步法标定局部寻优能力强,但标定结果受初值影响大的问题,基于粒子群算法,提出一种改进的星敏感器光学误差参数标定方法。该方法首先利用粒子群算法全局搜索能力强的优点,为光学误差的两步法标定提供一组次优的初值条件,然后将其代入两步法中,得到标定值。仿真结果表明:该方法能够有效解决两步法标定受初值条件影响大的不足,并能够提高标定结果的稳定性。
Aiming at the problem that the local optimization ability of the star sensor with two-step optical error calibration is strong but the calibration result is greatly influenced by the initial value, an improved calibration method of the optical error parameters of the star sensor is proposed based on particle swarm optimization. Firstly, this method uses the advantage of global search ability of particle swarm optimization to provide a set of suboptimal initial conditions for the calibration of optical error two-step method, and then substitutes it into the two-step method to get the calibration value. The simulation results show that this method can effectively solve the problem that the two-step calibration is greatly affected by the initial conditions and can improve the stability of calibration results.