Method for achieving ionospheric total electron content spatial feature extraction by utilizing conditional generative adversarial network

Abstract

The invention discloses a method for achieving ionospheric total electron content spatial feature extraction by using a conditional generative adversarial network. The method comprises the following steps: firstly, designing a deep learning model of a conditional coding and decoding generative adversarial neural network with spatial consideration, wherein the designed model is combined with an encoder-decoder structure with an adversarial learning thought, and deep features of input ionospheric total electron content sampling spatial data and complex interaction with a local structure mode canbe learned. Through example analysis, the effectiveness of the method is proved. Compared with a traditional method, the method successfully completes the extraction and reduction of the spatial characteristics of the total electron content of the ionized layer when facing the spatial distribution characteristics of the complex ionized layer, and has higher precision and smaller mean square errorin the aspect of realizing the spatial estimation of the total electron content of the ionized layer.

Description

technical field [0001] The invention belongs to the field of the ionosphere, and in particular relates to a method for extracting spatial features of the total electron content of the ionosphere by using a conditional generation confrontation network. Background technique [0002] The ionosphere is the part of the Earth's thermosphere ionized by solar radiation, filled with a large number of charged particles. The ionosphere is the undulating scattering medium of space radio signals, which will cause signal amplitude attenuation, phase delay, etc., and is an important source of space ranging errors for satellite navigation signals. The number of charged particles in the ionosphere is described by the total electron content of the ionosphere. When the ionosphere is affected by solar activities, geomagnetic activities, etc., it will cause fluctuations in the total electron content of the ionosphere. At present, ionospheric disturbance is a hot and difficult research topic in ...

AI Technical Summary

Problems solved by technology

In fact, the spatial distribution of the total electron content in the ionosphere is relatively complex. Affected by factors such as the solar radiation area and the distribution of geomagnetic intensity, its spatial distribution has strong nonlinearity, especially when the ionosphere is disturbed, it will produce strong The spatial gradient of the ionosphere further weakens the spatial linear correlation of the ionosphere. At this time, if the traditional feature extraction method is still used, it will inevitably cause a large error

The error of the total electron content of the ionosphere may be further amplified in the application and become a navigation ranging error, reducing the use accuracy of related applications

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