改进粒子滤波与预测滤波相结合的单星敏姿态估计  

Star-sensor-based attitude estimation fusing improved particle filter and predictive filter

作　　者：张惟[1,2] 林宝军[1] 

机构地区：[1]中国科学院光电研究院,北京100094 [2]中国科学院研究生院,北京100094

出　　处：《控制与决策》2011年第5期655-660,共6页Control and Decision

基　　金：国家自然科学基金项目(40374054);国家863计划项目(2008AA12A216)

摘　　要：针对卫星姿态估计的非线性、非高斯特性,提出一种粒子滤波和预测滤波相结合的估计方法,在无角速率测量时,首先利用预测方法在线估计系统模型误差和姿态角速度,再通过改进的规则化粒子滤波器估计姿态四元数.粒子初始化和重要性函数等的设计加快了算法的收敛速度,预测方法的引入有效降低了粒子维数.在某通用小卫星平台上进行仿真,并与扩展卡尔曼滤波(EKF)比较,所得结果表明,算法在不同初始姿态估计时具有较好的稳定性和收敛精度.算法还为粒子滤波和无陀螺定姿的研究提供了参考.For the characterization of nonlinear and non-Gaussian,an estimation method fusing particle filter and predictive filter is presented for the satellite attitude determination by using solely star sensor observations.At first,the system model error parameter and attitude angular rate are estimated online based on predictive filter.Then the attitude quaternion is estimated by the improved regularized particle filter.By designing proper particle initialization,important function,and so on,the algorithm convergence rate is speeded up.The dimension of the particle filter is effectively reduced by fusing predictive filter.A simulation is carried out on a general small satellite platform.Compared with the extended Kalman filter（EKF）,the result shows that the algorithm can converge fast and has good stable accuracy with respect to different initial attitude conditions.The algorithm provides reference to particle filter design and attitude determination without gyro as well.

关 键 词：无陀螺定姿 粒子滤波 预测滤波 小卫星 星敏感器 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]