一种模型误差鲁棒的卫星姿态确定方法

提出一种结合非线性预测滤波和二阶插值滤波实现基于星光/陀螺的高精度姿态确定的新算法.该算法用非线性预测滤波估计模型误差,再对补偿后的模型用高精度的二阶插值滤波来估计姿态参数.解决了在卫星实际运行中难以获得姿态确定系统的精确动力学模型,采用传统EKF(Extended Kalman Filter)将模型误差作为零均值白噪声处理,导致滤波精度降低甚至发散的问题.同时,二阶插值滤波将非线性模型按照二阶近似,无需计算函数偏导数,得到高精度的卫星姿态估计.仿真验证了该方法能有效地实时估计并补偿模型误差,提高了姿态估计的精度,且估计精度受滤波周期的影响不大,从而验证了算法的鲁棒性和有效性. 

Satellite attitude determination algorithm of model error robustness

A new high-precision star sensor/gyro attitude determination algorithm based on nonlinear predictive filter (NPF) and second-order divided difference filter was presented. In this algorithm, model error was estimated by NPF, and the state was estimated via second-order divided difference filter by using compensated system model. While it is difficult to obtain the accurate dynamic model of attitude determination system, extended Kalman filter (EKF) treated uncertain model error as zero-mean white noise might cause low precision of state estimation even to divergence. The proposed method solved such problem. The nonlinear model was the second-order approximation while using second-order divided difference filter without calculating the partial derivatives of system model, and then attitude estimation with high precision was obtained. Simulation results show that the proposed method estimates and compensates the model error in real-time effectively and improves the estimation precision, also the filter period influences the precision slightly. The effectiveness and the robustness of the proposed method are proved.