基于UPF滤波的微小航天器姿态矩阵估计方法

Micro-spacecraft Attitude Matrix Determination Algorithm Based on Unscented Particle Filter

针对基于惯性-星光姿态确定系统噪声存在非高斯分布的情况,提出了将离散粒子滤波(UPF)方法应用于定姿系统滤波器设计,该方法用离散卡尔曼滤波(UKF)得到粒子滤波的重要采样函数,从而克服扩展卡尔曼滤波(EKF)和UKF只能应用到噪声为高斯分布的不足。文章以微机电系统(MEMS)陀螺和互补性金属氧化物半导体有源像素图像传感器(CMOS APS)星敏感器为姿态敏感器件,选取基于矢量观测的最小参数姿态矩阵估计方法为定姿算法,提出将UPF与最小参数姿态矩阵估计方法结合,设计了一种针对微小航天器的UPF姿态估计器,采用从MEMS陀螺采集的数据进行了半物理仿真并对其特性进行了分析与比较。仿真比较结果表明:在敏感器精度较差并且系统噪声非高斯分布的情况下,这种基于UPF的姿态估计方法可以取得比EKF和UKF更快的滤波收敛性和更好的滤波精度,有效地提高了定姿性能。

In view of the problem that the noise of the inertial-stellar attitude determination system is non-Gaussian distribution, selected unscented particle filter（UPF）for the attitude determination system. UPF uses the unscented Kalman Filter （UKF）to generate sophisticated proposal distributions. It can avoid the limitation of the Extended Kalman Filter （EKF）and the UKF which only apply to Gaussian distributions. This paper takes the Micro Electro Mechanical System （MEMS） gyros and CMOS APS （Complementary Mental Oxide Semiconductor, Active Pixel Sensor）star sensor as attitude sensors, the method of attitude determination uses minimal-parameter attitude matrix estimation as the attitude parameter, presents a UPF attitude estimator. The attitude determination filter was constructed and semiphysical simulation was adopted from the MEMS gyro, and the unscented particle filter＇s characters are analyzed and compared with the EKF and the UKF. The simulation results show the system performance is improved by the suggested method. The convergence rate and the determination accuracy are higher than the EKF and the UKF.