Evaporative waveguide profile estimation method based on deep neural network

Abstract

The invention relates to an evaporation waveguide profile estimation method based on a deep neural network. An evaporation waveguide correction refractive index profile and an evaporation waveguide height can be rapidly estimated in a specified ocean area. The method aims at overcoming the defect that an existing evaporation waveguide model is low in iterative computation efficiency. A climate prediction system of the American national environment prediction center is utilized. Analyzing the meteorological data and the atmospheric correction refractive index calculated by the evaporation waveguide calculation model; training deep neural networks, the accuracy of the method is verified by using the data of the random position in the training area (except the training point); the evaporationwaveguide profile estimation method based on the deep neural network can be used for evaporation waveguide prediction of large-area sea areas, the height distribution condition of the evaporation waveguide is monitored in real time, a communication strategy can be conveniently and rapidly changed according to the change of the evaporation waveguide condition, and the maritime beyond-visual-rangecommunication quality is guaranteed.

Description

technical field [0001] The invention belongs to the technical fields of offshore surface evaporation waveguide, atmospheric correction refractive index, maritime over-the-horizon communication, evaporation waveguide calculation model, deep learning, etc., and relates to an evaporation waveguide profile estimation method based on a deep neural network. It involves a large-area evaporation waveguide profile estimation method, using high-resolution reanalysis of meteorological data, combined with the basic equations of atmospheric motion, atmospheric boundary layer similarity theory, modified refractive index theory, evaporation waveguide calculation model, standard normalization method, depth A method for estimating the profile of the evaporation waveguide is obtained by using neural network algorithm and so on. When the meteorological data of a large area of ​​the sea is used as input, it can be used for real-time monitoring of the sea evaporation duct. Background technique ...

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Problems solved by technology

[0007] In order to avoid the deficiencies of the prior art, the present invention proposes a method for estimating the evaporation waveguide profile based on a deep neural network, which solves the problem of low calculation efficiency of the traditional evaporation waveguide profile prediction model and difficulty in estimating the evaporation waveguide profile in large-area sea areas

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