摘要
在以太阳作为目标源的天文测速导航中,多普勒速度量测量存在较多野值误差,严重影响导航精度。对此,提出一种基于高斯过程回归与无迹卡尔曼滤波(Gaussian process regression and unscented Kalman filtering,GPR-UKF)的野值检测修复方法,建立速度量测量的动态预测模型。此外,还针对不同参数对模型精度的影响进行研究。经仿真验证,所提方法效果显著优于传统野值处理方法。
In astronomical velocity measurement navigation with the Sun as the target source,there are many outliers in Doppler velocity measurement,which seriously affects the accuracy of navigation.Thus,a outlier detection and repair method based on Gaussian process regression and unscented Kalman filtering(GPR-UKF)is proposed to establish a dynamic prediction model for velocity measurement.In addition,the impact of different parameters on the accuracy of the model is researched.Simulation verification test demonstrates that the proposed method has better performance than traditional outlier processing methods.
作者
张寿健
桂明臻
ZHANG Shoujian;GUI Mingzhen(School of Automation,Central South University,Changsha 410083,China;Key Laboratory of Smart Earth,Beijing 100029,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2024年第12期4183-4191,共9页
Systems Engineering and Electronics
基金
智慧地球重点实验室基金(KF2023ZD01-01)资助课题。
关键词
组合导航
高斯过程回归
无迹卡尔曼滤波
太阳多普勒速度
星光角距
野值处理
integrated navigation
Gaussian process regression(GPR)
unscented Kalman filtering(UKF)
solar Doppler velocity
starlight angle pitch
outlier processing
