基于SSUKF的航天器自主导航算法

针对航天器自主导航系统对稳定性、精确性和实时性的要求,将超球面分布采 样点变换SSUT(Spherical Simplex Unscented Transformation)和Unscented卡尔曼滤波(UK F)相结合,研究了基于SSUT的UKF(SSUKF)导航滤波算法.由于SSUT减少了采样点个 数,在保证滤波精度和标准UKF相当的条件下减轻了计算负担.根据UKF和扩展卡尔曼滤波(E KF)计算过程相似的特点,设计了SSUKF和EKF相结合的混合卡尔曼滤波算法.算法通过能够 度量估计误差的模式切换函数,可以自适应地在SSUKF和EKF之间切换,避免了UKF计算效率低 以及EKF对滤波参数敏感、容易发散的缺点.数值仿真结果表明,混合卡尔曼滤波器提高了 计算效率,保证了估计精度,具有良好的鲁棒性,适合于航天器自主导航系统. 

Spacecraft autonomous navigation based on SSUKF algorithm

An improved unscented Kalman filter(UKF) algorithm based on spherical simplex unscented transformation(SSUT) was presented to satisfy the stability, precision and real time requirements of spacecraft autonomous navigation.The SSUT reduces the sigma points for the unscented transformation.Compared with the traditional UKF,the computational efficiency of spherical simplex unscented Kalman filter(SSUKF) was improved,without sacrificing the estimation precision.According to the similar computation process of UKF and extended Kalman filter(EKF),the combined Kalman filter based on SSUKF and EKF was designed.This algorithm can switch adaptively between the SSUKF and EKF through a mode switching function which is the criterion of estimation errors.It solves both the inefficiency problem of UKF and the instability problem of EKF.Simulation results show that the combined Kalman filter integrates the advantages of SSUKF and EKF,improves the computational efficiency,insures the estimation precision and robustness,therefore it is more suitable for the spacecraft autonomous navigation system.