A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

被引：0
|
作者：
Ma, Tao [1 ,2 ]
Liu, Heng [1 ,3 ]
Zhang, Yu [1 ,3 ]
机构：
[1] Henan Normal Univ, Coll Elect & Elect Engn, Xinxiang, Henan, Peoples R China
[2] Key Lab Optoelect Sensing Integrated Applicat Hen, Xinxiang, Henan, Peoples R China
[3] Acad Workstn Electromagnet Wave Engn Henan Prov, Xinxiang, Henan, Peoples R China
来源：
PROGRESS IN ELECTROMAGNETICS RESEARCH M | 2019年 / 86卷
关键词：
PREDICTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A method based on the radial basis function neural network (RBFNN) is developed to fast establish the tropospheric refractivity profile model. Parameters of the RBFNN include SPREAD, and the number of training samples is optimized. The actual measured data of meteorological station at Qingdao city in China are used as test data to evaluate the performance of RBFNN. The simulation results show that the root mean squared error (RMSE) has a minimum of 0.81 when SPREAD is 8.1. The simulated values agree well with the test data which is observed by using the sounding balloon method. Finally, the tropospheric refractivity profile model of a selected area is established by using two different simulation methods. This paper attempts to propose a method to fast establish the tropospheric refractivity profile model which provides an available method to correct the atmospheric refraction error in radar applications.
引用
收藏
页码：93 / 102
页数：10
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