无陀螺卫星的非线性姿态估计算法及仿真研究

Nonlinear Attitude Estimation Algorithm for Gyroless Satellite and Its Simulation

无陀螺卫星姿态模型的强非线性会严重降低常用滤波器的精度和收敛性,在初始误差较大的情况下甚至会导致姿态估计失败,针对该问题,引入一种新的非线性估计算法。该算法利用迭代数值方法解决了当前和过去采样点的非线性平滑问题,保留了当前采样点之前一定数量阶段的所有非线性特性,而没有进行任何近似。为提高计算速度,分析了影响计算速度的原因,并给出加速计算的方法。通过无陀螺卫星的姿态估计仿真,结果证明,新算法能在较大初始状态误差和卫星动力学模型高度不确定性的情况下,对无陀螺卫星姿态角和角速度的估计具有显著的收敛性和精度。

Aim. The severe nonlinearities of gyroless satellite attitude model, which degrade the convergence and accuracy of EKF （extended Kalman filter） and UKF （unscented Kalman filter） respectively, and the large initial state errors sometimes cause estimation failure. We adopt a new kind of nonlinear estimation algorithm to deal with the problem. Section 2 of the full paper explains our new estimation algorithm. Subsection 2. 2 solves a nonlinear smoothing problem for the current and previous sample inter,~als using iterative numerical techniques and retains the nonlinearities of a number of stages that precede the stage of interest without any approximation. Subsection 2. 3 accelerates computing speed through analyzing the influencing factors. The results of simulation, given in Figs. 1 through 3, illustrate preliminarily that the new algorithm exhibits good convergence and accuracy for gyroless satellite attitude estimation problems that have large initial state errors and severe nonlinearities of satellite＇ s dynamics model.