An iterative attitude estimation algorithm was proposed to solve multiplicative stochastic matrix in satellite attitude determination system. Based on the principle of linear minimum-variance and projection formula, the attitude iterative filter algorithm was deduced. By linearizing and iterating two times respectively for state equation and measure equation, the algorithm can reduce the influence of linearization error to the attitude determination precision. The algorithm enlarges the application range of EKF in the state estimation of nonlinear systems with multiplicative noise. Simulation results indicate that the proposed algorithm provides effeclive performance in the presence of multiplicative stochastic matrix.%针对带乘性噪声的星敏感器/陀螺非线性卫星姿态确定系统,提出了一种迭代MEKF(Iterative Multiplicative Extended Kalman Filter)姿态估计滤波算法.通过对带乘性噪声的非线性卫星姿态确定系统的状态方程和测量方程进行二次线性化迭代,并基于线性最小方差准则和投影公式,导出了姿态状态递推滤波算法,解决了线性化误差对姿态滤波精度的影响,并扩展了EKF算法在带乘性随机噪声阵的非线性系统状态估计中的应用范围.仿真表明,迭代MEKF滤波算法能够有效地克服乘性噪声对姿态估计精度的影响.
展开▼
