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An Automatic Detection Method for Morse Signal Based on Machine Learning

  • Zhihao Wei
  • Kebin JiaEmail author
  • Zhonghua Sun
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)

Abstract

In this paper, an automatic detection for time-frequency map of Morse signal is proposed base on machine learning. Firstly, a preprocessing method based on energy accumulation is proposed, and the signal region is determined by nonlinear transformation. Secondly, the feature extraction of different types of signal time-frequency maps is carried out based on the graphics. Finally, a signal detection classifier is built based on the feature matrix. Experiments show that the classifier constructed in this paper has the generalization ability and can detect the Morse signal in the broadband shortwave channel, which improve the accuracy of Morse signal detection.

Keywords

Morse signal Machine learning Feature extraction Classifier 

Notes

Acknowledgments

This paper is supported by the Project for the Key Project of Beijing Municipal Education Commission under Grant No. KZ201610005007, Beijing Postdoctoral Research Foundation under Grant No.2015ZZ-23, China Postdoctoral Research Foundation under Grant No. 2016T90022, 2015M580029, Computational Intelligence and Intelligent System of Beijing Key Laboratory Research Foundation under Grant No.002000546615004, and The National Natural Science Foundation of China under Grant No.61672064.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.College of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Beijing Laboratory of Advanced Information NetworksBeijingChina
  3. 3.Beijing Advanced Innovation Center for Future Internet TechnologyBeijingChina

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