摘要
卫星导航信号与定位目标信号在电离层及对流层的传播时延是全球导航卫星系统(Global Navigation Satellite System,GNSS)定位的重要误差源。对这类信号传播时延进行估计,是改善系统定位精度的重要手段。常规的时延估计方法根据霍普菲尔德、萨斯塔莫宁等经验模型计算信号时延估计值,由于参数固定,模型对大气层变化的敏感度十分低,存在估计精度不够高,鲁棒性差等问题。针对经验模型无法对估计参数实时调整的缺点,提出了一种基于经验模型与数字高程的卫星导航信号与定位目标信号自适应时延估计方法。在经验模型估计量的基础上对数字高程值进行估计,并构建自适应卡尔曼滤波器形成误差反馈环,通过迭代计算自适应修正经验模型参数。仿真结果表明,改进的信号时延估计方法能更好地跟踪大气层参数变化,估计误差优于常规经验模型估计结果,有效地提高了卫星导航信号与定位目标信号的定位性能。
The propagation delay of the satellite navigation signal and the positioning target signal in the ionosphere and troposphere is an important error source for the positioning of the Global Navigation Satellite System(GNSS).Estimating the propagation delay of such signals is an important means to improve the system’s positioning accuracy.The conventional time-delay estimation method estimates the signal delay estimation based on experience models such as Hopefield and Saastamoinen models.Due to the fixed parameters,the sensitivity of the model to atmospheric changes is very low,so the estimation accuracy is not high enough and the robustness is poor.Aiming at the shortcoming that the empirical model can not adjust the estimation parameters in real time,this paper proposes an adaptive time delay estimation method for satellite navigation signals and positioning target signals based on empirical models and the digital elevation.Based on the estimation of the empirical model,the digital elevation was estimated,and an adaptive Kalman filter is constructed to form an error feedback loop.The parameters of the empirical model was adaptively modified through iterative calculation.The simulation results show that the improved signal delay estimation method can track the variation of atmospheric parameters well,and the estimation error is better than the conventional model estimation results,which can effectively improve the positioning performance of satellite navigation signals and positioning target signals.
作者
邓力
王钦
DENG Li;WANG Qin(College of Civil Safety Engineering,Civil Aviation Flight University of China,Guanghan Sichuan 618307,China)
出处
《计算机仿真》
北大核心
2019年第5期113-116,共4页
Computer Simulation
