摘要
针对单频地基增强系统(Ground Based Augmentation System,GBAS)中电离层异常时Hatch滤波器平滑精度降低问题,系统分析了电离层延时对Hatch滤波器平滑精度的影响,提出一种改进自适应Hatch滤波算法。根据卫星信号计算码载偏离度,并利用二阶线性时不变低通滤波器抑制码载偏离度高频信号,以实现电离层异常实时检测;建立平滑后伪距误差均方根与电离层延时变化率、伪距测量噪声标准差以及平滑时间三者之间的函数模型,并由此确定出Hatch滤波器最优平滑时间。利用GBAS原理样机进行验证实验,结果表明:自适应Hatch滤波算法能够根据卫星信号电离层延时变化率确定滤波器最优平滑时间,且当电离层异常时,自适应Hatch滤波器机载位置误差最大由1.15 m减小为0.43 m,从而验证了所提算法的有效性。
Aimed at the problem that the smoothing accuracy of Hatch filter in single frequency GBAS(ground based augmentation system)is reduced by the ionospheric anomaly,the influence of ionospheric delay on the smoothing accuracy of Hatch filter was analyzed systematically,and an improved adaptive Hatch filter algorithm was proposed.The code-carrier divergence was calculated according to the satellite signal,and its high frequency was suppressed by using the second-order linear time-invariant filter to detect whether the ionosphere is abnormal.A function model between the root mean square of smoothed pseudorange error and the ionospheric delay rate,the standard deviation of pseudorange measurement noise and the smoothing time was established,from which the optimal smoothing time of Hatch filter was determined.The verification experiments were carried out by using GBAS prototype developed in the laboratory.The experimental results show that the adaptive Hatch filter algorithm is able to calculate the optimal smoothing time according to the ionospheric delay rate of satellite signal.When the ionospheric is abnormal,the maximum airborne position error is reduced from 1.15 m to 0.43 m,which verifies the effectiveness of the proposed algorithm.
作者
胡杰
朱倚娴
单尧
HU Jie;ZHU Yixian;SHAN Yao(The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China;State Key Laboratory of Air Traffic Management System and Technology, Nanjing 210007, China;School of Mechanical Engineering, Nantong University, Nantong 226019, China)
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2020年第4期115-122,共8页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(61903204)
国家重点研发计划资助项目(2017YFB0503401)。
