模糊自适应无迹卡尔曼滤波方法用于天文导航

Application of fuzzy adaptive unscented Kalman filter in spacecraft celestial navigation

摘要:

为克服航天器自主天文导航中不确定测量噪声对导航精度的影响,提出了一种基于模糊推理的自适应无迹卡尔曼滤波(FUKF)方法.该方法根据滤波过程中实际测量残差方差与理论残差方差的比值,将系统滤波过程分为普通模式和自适应模式.分别对两种模式建立模糊隶属度函数,应用模糊推理规则,得到自适应修正因子,对系统的测量噪声方差阵进行实时修正,使其跟踪实际测量噪声的变化.当系统受到不确定环境噪声影响时,该滤波算法仍然有效收敛.将该方法应用于直接敏感地平的航天器自主天文导航中,不同测量噪声水平下的仿真结果表明,该算法对不确定的测量噪声具有较强的自适应能力,保证了导航信息的输出精度.

Abstract:

To solve the problem of unscented Kalman filter(UKF) filtering under uncertain measurement noises during spacecraft autonomous celestial navigation,an adaptive UKF algorithm based on fuzzy logic is presented.According to the ratio between the actual residual and filter residual,the filter process is divided into two modes: normal mode and adaptive mode.The fuzzy membership functions are established separately to the two modes.Through fuzzy inference rule,the adaptive revise factor is obtained to real-time revise the measurement noise covariance so that the actual noise covariance is tracked.Accordingly,the filter could converge effectively even the measurements are suffered from uncertain noises.The fuzzy adaptive UKF algorithm is applied in directly sensing horizon celestial navigation system,and simulation results under different noise levels show that the algorithm is adaptive to uncertain measurement noises.