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Solar radio storm detection method based on support vector machine model

A support vector machine and solar radio technology, applied in the field of solar radio storm detection, can solve the problems of high cost, inability to monitor solar radio in real time around the clock, and sparse distribution.

Pending Publication Date: 2020-07-17
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

[0004] The main method of traditional detection of solar radio activity is to rely on radio telescopes, but the cost is expensive, the distribution is sparse, and it is impossible to monitor the solar radio in real time around the clock

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  • Solar radio storm detection method based on support vector machine model
  • Solar radio storm detection method based on support vector machine model
  • Solar radio storm detection method based on support vector machine model

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Embodiment Construction

[0055] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0056] The invention provides a solar radio burst detection method based on a support vector machine model. When a solar radio burst occurs, various inspection items generated by GNSS signals are comprehensively inspected to detect whether the solar radio burst occurs. This method is feasible for the detection of solar radio storms. The detection results can not only show the occurrence time of a single station solar radio storm, but also show whether multiple stations are affected by the solar radio storm at the same time. Compared with the traditional method, this method is low in cost, combines multiple factors, and the recognition accuracy and efficiency are relatively improved.

[0057] This embodiment discloses a solar radio storm detection method based on a support vector machine model, such as figure 1 shown, including:

[0058] Step 1...

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Abstract

The invention discloses a solar radio storm detection method based on a support vector machine model, the support vector machine is a binary classification model, and the geometric interval from a sample point closest to a hyperplane to the plane can be maximized. According to the method, whether the solar radio storm occurs or not is judged by utilizing the influence of the solar radio storm on the GPS / BDS signal carrier-to-noise ratio, the positioning error, the geometric accuracy factor and the satellite lock loss in combination with an SVM classification algorithm. In the process, firstly,the carrier-to-noise ratio of an observation earth satellite, positioning errors in three directions, geometric accuracy factors and the number of unlocked satellites are input; data preprocessing isconducted; feature vectors are obtained; and whether solar radio storms happen or not is marked. And inputting the samples into an SVM classifier for learning to obtain an optimal classifier. And when the new feature vector enters the classifier, automatic classification is carried out. The determination method can realize all-weather detection, is high in efficiency and simple in process, does not depend on a radio telescope, and is low in cost.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a method for detecting solar radio bursts based on a support vector machine model. Background technique [0002] With the increasing application of GNSS and other satellite technologies in modern society, the influence of solar radio storm intensity on GNSS signals has become an important part that cannot be ignored. The observation of solar radio bursts plays a vital role in the study of solar physics and the monitoring of space weather environment, and has important scientific research value in space physics. Solar radio bursts are a phenomenon of ray enhancement and radio noise sharply increased when a sudden strong disturbance occurs on the sun, and often occur together with solar flares, X-ray bursts, and even proton bursts or cosmic ray bursts. . Solar radio bursts are generally not a single mechanism, but a combined effect of multiple mechanisms. During m...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214Y02D30/70
Inventor 祝雪芬罗铱镅林梦颖杨帆
Owner SOUTHEAST UNIV
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