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A METHOD FOR FAST ESTABLISHING TROPOSPHERIC REFRACTIVITY PROFILE MODEL BASED ON RADIAL BASIS FUNCTION NEURAL NETWORK

By T. Ma, H. Liu, and Y. Zhang

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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.

Citation:
T. Ma, H. Liu, and Y. 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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