基于Marginalized粒子滤波的卫星姿态估计算法

针对具有矢量观测的卫星姿态估计问题。提出一种基于Marginalized粒予滤波（MPF）的算法．采用Rao-Blackwellization技术，将卫星模型状态向量中的线性状态部分（陀螺漂移）和非线性状态部分（卫星姿态）分开处理，从而使得估计的方差降低．以较少的运算量获得较好的估计效果．通过引入解决含等式约束条件的估计问题方法，保证了姿态四元数的归一化．将所提出的方法应用于某型号卫星．仿真验证了用该算法处理卫星姿态估计问题的优越性．

Satellite attitude estimation based on marginalized particle filter

An algorithm based on marginalized particle filters(MPF) is presented to solve satellite attitude and gyro bias estimation problem with vector observations.By marginalizing out the state appearing linearly in satellite model,attitude vector is approximated by a set of particles and estimated using particle filter,while estimation of gyro bias is obtained for each one of attitude particles by applying the Kalman filter,which is associated with each particle in order to reduce the size of the state space and computational burden.The method of estimation with equation constraint is employed due to the normality constraint of attitude quaternion.The numerical simulation of a rigid satellite with gyro and three-axis-magnetometers,shows the superiorty of the proposed algorithm in coping with the nonlinearity of model.