重力测量卫星KBR系统相位中心在轨标定算法

针对低低跟踪(SST-LL)重力测量卫星K频段测距(KBR)系统相位中心在轨标定问题,提出了一种应用预测卡尔曼滤波算法的KBR系统在轨标定算法。首先,以磁力矩器和姿态控制喷气发动机为执行部件,对一颗卫星施加一定的组合力矩,使其绕另一颗卫星进行周期性姿态机动;然后,将星敏感器数据代入预测卡尔曼滤波算法中估计出卫星姿态;最后,根据KBR系统观测值与卫星姿态角之间的关系,利用扩展卡尔曼滤波算法估计出KBR系统相位中心的位置。数值仿真结果表明:KBR系统相位中心可以被实时估计,当存在较大的卫星姿态动力学模型误差时,KBR系统相位中心的标定误差仍在0.3mrad以内,证明此算法估计精度较高且鲁棒性强。

Algorithm for KBR System Phase Center In-orbit Calibration of Gravity Measurement Satellite

An algorithm for in-orbit calibration of KBR （K-band ranging） system of the SST-LL （low-low satellite-to-satellite tracking） gravity measurement satellite based on PEKF （predictive Kalman filter） algorithm is proposed. The proposed algorithm is embodied in three steps. Firstly, the magnetic torque and thruster torque are applied to force the satellite to follow the designed atti- tude track. Secondly, the observation of star sensor is used to estimate attitude based on PEKF. Thirdly, EKF （extended Kalman filter） algorithm is used to estimate the KBR system phase cen- ter by using of the relationship between the observation of KBR system and satellite attitude mo- tion angle. The numerical results indicate that phase center can be real-time estimated and the calibration error of KBR system phase center is less than 0.3mrad even when the uncertainty of satellite attitude dynamics model is big, which demonstrates the effectiveness and robust of the proposed algorithm.