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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2019-10-30
A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network
By
Tao Ma,

    Dr. Tao Ma

    College of Electronic and Electrical Engineering

    Henan Normal University

    China

    Homepage

Heng Liu,

    Mrs. Heng Liu

    College of Electronic and Electrical Engineering

    Henan Normal University

    China

    Homepage

Yu Zhang

    Prof. Yu Zhang

    College of Physics and Information Engineering

    Henan Normal University

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 86, 93-102, 2019
doi:10.2528/PIERM19072304 | Google Scholar

Abstract
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 valuesagree 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 providesanavailablemethod to correctthe atmospheric refractionerror in radar applications.
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Citation
Tao Ma, Heng Liu, and Yu Zhang, "A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network," Progress In Electromagnetics Research M, Vol. 86, 93-102, 2019.
doi:10.2528/PIERM19072304
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