基于BP神经网络的对流层折射率预测方法研究

Research on tropospheric refractivity prediction method based on BP neural network

对于卫星导航系统,定位误差受对流层大气折射率影响,提高对流层大气折射率预测的精确性能够降低导航定位误差.对流层大气折射率是研究对流层对电磁波传播影响的主要参数,其预测的精确性对于无线电系统有重要意义.本文提出一种基于反向传播(back propagation,BP)的对流层折射率预测方法,将年、月、日、时刻、地表折射率、海拔高度作为网络模型的输入,输入海拔高度处的折射率作为模型的输出;类似地,通过调整输入和输出参数,还可以利用BP神经网络预测近地面1 km折射率梯度.在此基础上,利用香港地区和太原地区历史探空气象数据对新提出算法进行了计算分析,并与现有文献中的方法作了比较,结果表明:本文提出的方法在计算的精确性方面有一定的优势.

For satellite navigation systems,positioning errors are affected by the refractive index of the troposphere atmosphere.Improving the accuracy of predicting the refractive index of the troposphere atmosphere can reduce navigation positioning errors.The refractivity of tropospheric atmosphere is the main parameter for studying the influence of the troposphere on the propagation of electromagnetic waves,and the accuracy of its predictions is of great significance for radio systems.In this paper,a tropospheric refractivity prediction method based on BP neural network is proposed,which takes the year,month,day,time,surface refractivity,and altitude as the input of the BP neural network,and the corresponding refractivity at the input altitude as the output of the model.Similarly,by adjusting the input and output parameters,the BP neural network can also be used to predict the refractivity gradient of 1 km near the ground.Finally,the proposed algorithm is calculated and analyzed by using the historical aerial exploration data of Hongkong and Taiyuan,and compared with the methods in the existing papers.The results show that the proposed method has certain advantage in the calculation accuracy.