Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network

Ling YOU; Weihao LI; Wenlin ZHANG; Keren WANG

doi:10.11999/JEIT190658

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2643-2648

Ling YOU, Weihao LI, Wenlin ZHANG, Keren WANG. Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2643-2648. doi: 10.11999/JEIT190658

Citation:

Ling YOU, Weihao LI, Wenlin ZHANG, Keren WANG. Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2643-2648. doi: 10.11999/JEIT190658

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Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network

doi: 10.11999/JEIT190658

Ling YOU²,
Weihao LI^{1, 2
,
,},
Wenlin ZHANG¹,
Keren WANG²

1.
PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
2.
National Key Laboratory of Science and Technology on Blind Signal Processing, Chengdu 610041, China

Funds: The National Natural Science Foundation of China (61403415), The Postdoctoral Science Foundation of China(2016M602975)

Received Date: 2019-08-29
Rev Recd Date: 2020-05-08

Available Online: 2020-05-28

Publish Date: 2020-11-16

Abstract

Abstract

In the military and civilian fields, the Morse telegraph is always as an important means of short-wave communication, but the current automatic decoding algorithms still have problems such as low accuracy, inability to adapt to low signal-to-noise ratio and unstable signals. A deep learning method is introduced to construct a Morse code automatic recognition system. The neural network model consists of convolutional neural network, bidirectional long short-term memory network and connectionist temporal classification layer. The structure is simple and can implement end-to-end training. Related experiments show that the decoding system can achieve good recognition results under different signal-to-noise ratio, code rate, frequency drift and code length deviation caused by different sending manipulation, and the performance is better than the traditional recognition algorithms.
- Morse code,
- Automatic decoding,
- Deep learning,
- Frequency drift,
- Code length deviation

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References(17)

References

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