The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 01, 2020

Filed:

Jul. 16, 2020
Applicant:

Beihang University, Beijing, CN;

Inventors:

Zhipeng Wang, Beijing, CN;

Cheng Wang, Beijing, CN;

Kaiyu Xue, Beijing, CN;

Kun Fang, Beijing, CN;

Assignee:

BEIHANG UNIVERSITY, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01S 19/07 (2010.01); G01W 1/10 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G01S 19/072 (2019.08); G01W 1/10 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01);
Abstract

The present invention provides a global ionospheric total electron content prediction system based on a spatio-temporal sequence hybrid framework. The prediction system implements computational processing for two types of spatio-temporal sequences, wherein for a stationary spatio-temporal sequence, a STARMA model prediction method is constructed in the present invention; for a non-stationary spatio-temporal sequence, a nonlinear spatio-temporal trend is firstly extracted from the non-stationary spatio-temporal sequence by adopting a ConvLSTM method until the extracted residual passes a stationarity test, and then the electron content is predicted using the STARMA model prediction method. By using a parallel computing method in the present invention, the computational efficiency can be greatly improved, and the operation time can be saved; meanwhile, the global ionospheric electron content distribution characteristics are fully considered, so that the ionospheric prediction algorithm itself is more in line with the space weather law and has a higher prediction accuracy.


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