Automatic Modulation Classification Exploiting Hybrid Machine Learning Network
Joint Authors
Wang, Feng
Huang, Shanshan
Wang, Hao
Yang, Chenlu
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-04
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
It is a research hot spot in cognitive electronic warfare systems to classify the electromagnetic signals of a radar or communication system according to their modulation characteristics.
We construct a multilayer hybrid machine learning network for the classification of seven types of signals in different modulation.
We extract the signal modulation features exploiting a set of algorithms such as time-frequency analysis, discrete Fourier transform, and instantaneous autocorrelation and accomplish automatic modulation classification using naive Bayesian and support vector machine in a hybrid manner.
The parameters in the network for classification are determined automatically in the training process.
The numerical simulation results indicate that the proposed network accomplishes the classification accurately.
American Psychological Association (APA)
Wang, Feng& Huang, Shanshan& Wang, Hao& Yang, Chenlu. 2018. Automatic Modulation Classification Exploiting Hybrid Machine Learning Network. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1208241
Modern Language Association (MLA)
Wang, Feng…[et al.]. Automatic Modulation Classification Exploiting Hybrid Machine Learning Network. Mathematical Problems in Engineering No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1208241
American Medical Association (AMA)
Wang, Feng& Huang, Shanshan& Wang, Hao& Yang, Chenlu. Automatic Modulation Classification Exploiting Hybrid Machine Learning Network. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1208241
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208241