空间引力波探测航天器高精度状态估计器设计

High Precision State Estimation Method Design for Space-Based Gravitational Wave Detection Spacecraft

基于星敏感器和惯性传感器等多源异构数据,提出了一种线性化四元数量测的高性能卡尔曼滤波算法。根据空间引力波探测超稳超静平台特性,通过对星敏感器四元数测量在航天器系统姿态小角度变化下的近似变形构造新的线性伪量测,从而满足卡尔曼滤波器的线性假设条件。结合航天器系统离散时间状态空间模型以及多传感器数据,设计线性化四元数量测的卡尔曼滤波算法,以实现航天器系统状态的高精度在轨估计。通过仿真实验以及性能分析验证了该算法可以有效提高系统状态量估计精度,满足空间引力波探测任务航天器姿态量测的精度需求,为高精度控制提供高精度观测信息。

Based on multi-sensor data from star sensors and inertial sensors,a high-performance Kalman filtering algorithm with linearized quaternion measurements was proposed in this paper.According to the ultra-stable and ultra-static platform characteristics of the task,new pseudo-linear measurements were constructed by an approximate transformation for quaternion measurements under small angle change of the spacecraft so that the linear assumption of Kalman filtering was satisfied.Combined with the discrete-time state space model of spacecraft system and the multi-sensor measurements,a Kalman filtering algorithm with linearized quaternion measurements was designed,to achieve high-precision in-orbit state estimation of spacecraft system.The simulation experiments are provided to demonstrate the effectiveness of the proposed Kalman filtering algorithm,which meets the precision requirement of spacecraft attitude estimation for space-based gravitational waves detection and provides the high-precision observation for spacecraft attitude control.