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.
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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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Wei, Z., Jia, K., Sun, Z. (2018). An Automatic Detection Method for Morse Signal Based on Machine Learning. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-63859-1_24
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DOI: https://doi.org/10.1007/978-3-319-63859-1_24
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