Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

An ionospheric phase scintillation detection method based on nonlinear svm algorithm

A technology of phase flicker and detection method, which is applied in computing, computer components, instruments, etc., can solve the problems of phase flicker and the accuracy needs to be improved, and achieve the effect of high accuracy

Active Publication Date: 2021-07-27
SOUTHEAST UNIV
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The most common ionospheric phase scintillation detection method is based on experience, by judging the phase scintillation index Whether it exceeds a certain threshold, if it exceeds the threshold, it is judged that phase flicker has occurred. Since this method is limited by the non-optimized empirical threshold, its accuracy needs to be improved

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An ionospheric phase scintillation detection method based on nonlinear svm algorithm
  • An ionospheric phase scintillation detection method based on nonlinear svm algorithm
  • An ionospheric phase scintillation detection method based on nonlinear svm algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] Below in conjunction with accompanying drawing, a kind of ionospheric phase scintillation detection method based on nonlinear SVM algorithm proposed by the present invention is described in detail:

[0049] The present invention implements a nonlinear SVM algorithm based on machine learning, and its overall structure is as follows: figure 1 As shown: First, the signal measured in the receiver is processed to extract the characteristic parameters and marked. The specific method is as follows: use the phase data to detrend the signal measured in the receiver, design the low bandwidth of the receiver carrier loop, and geometric phase After center offset calibration and other technologies, get new carrier phase data Calculate the ionospheric phase scintillation index every 30s Calculated as follows:

[0050]

[0051] Among them, E(·) represents the mathematical expectation, and the time length is taken as 30s.

[0052] Divide all processed data into 3-minute blocks ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention proposes an ionospheric phase scintillation detection method based on a nonlinear SVM algorithm. The method uses the nonlinear SVM algorithm in machine learning to judge the phase scintillation of the detected signal. The purpose of the SVM algorithm is to pass a given For samples with certain characteristics, find a hyperplane to classify the samples and apply them to new samples. In this process, the received GPS signal is first processed by high-pass filtering and calculated to obtain the maximum value and average value of the phase scintillation index, which is used as a learning sample to label the corresponding scintillation event, and set the label to 1 or ‑1 , indicating that the phase scintillation event has occurred or not, and then the samples are input into the nonlinear SVM classifier for learning, and the optimal classifier is obtained. When the new phase scintillation event feature vector enters the SVM classifier, it will be automatically classified. The detection method can classify a large number of scintillation events at the same time, and the use of nonlinear SVM algorithm improves the accuracy of the classification model.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to an ionospheric phase scintillation detection technology based on a nonlinear SVM algorithm. Background technique [0002] With the development of the GPS system and the demand for higher positioning accuracy, the monitoring and research of ionospheric scintillation needs to be paid attention to and valued. Ionospheric scintillation is caused by the irregular structure in the ionospheric plasma, and refers to the rapid fluctuation of the amplitude and phase of the radio frequency signal propagating in the ionosphere. There are many reasons for this phenomenon, including but not limited to solar activity, magnetic storms, local electric fields, electrical conductivity, wave interactions, etc. The high-frequency occurrence areas of ionospheric scintillation are near the equator and the polar regions, and the amplitude scintillation and phase scintillation...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 祝雪芬林梦颖陈熙源汤新华
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products