![](/images/graphics-bg.png)
Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-12
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
A method for satellite downlink signal detection based on a generative adversarial network is proposed.
The generator adversarial network and adversarial network are established, respectively.
The generator network realizes the local generator of satellite signals, and the adversarial network is used for high-precision signal detection.
The error network is generated by the error signal to form the satellite link downlink.
The network reconstructs the optimal weights by generating errors, forms an error matrix for different satellite downlink, and then forms an adaptive matrix weight adjustment.
Through the reconstruction of the optimal detection matrix, detection for the downlink signals of multiple satellites is completed.
The proposed generative adversarial network can realize the high-precision detection for the downlink signal.
American Psychological Association (APA)
Guan, Qing-yang& Shuang, Wu. 2020. Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1202464
Modern Language Association (MLA)
Guan, Qing-yang& Shuang, Wu. Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1202464
American Medical Association (AMA)
Guan, Qing-yang& Shuang, Wu. Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1202464
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202464