Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network
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摘要: 在军用和民用领域，Morse电报一直是一种重要的短波通信手段，但目前的自动译码算法仍然存在准确率低、无法适应低信噪比和不稳定的信号等问题。该文引入深度学习方法构建了一个Morse码自动识别系统，神经网络模型由卷积神经网络、双向长短时记忆网络和连接时序分类层组成，结构简单，且能够实现端到端的训练。相关实验表明，该译码系统在不同信噪比、不同码速、信号出现频率漂移以及不同发报手法引起的码长偏差等情况下，均能取得较好的识别效果，性能优于传统的自动识别算法。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.
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Key words:
- Morse code /
- Automatic decoding /
- Deep learning /
- Frequency drift /
- Code length deviation
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表 1 CNN层设置
层名称 对应核大小 卷积层1 (5, 5, 1, 32)，步长=(1, 1) 最大池化层1 (2, 2)，步长=(2, 2) 卷积层2 (5, 5, 32, 64)，步长=(1, 1) 最大池化层2 (2, 16)，步长=(2, 2) 
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表 2 数据集组成
码速(wpm) 信噪比(dB) 数目 训练集 25, 30, 40 40, 30, 20, 10, 6, 3, –3, –6, –8, –10 25000/50000 验证集 25, 30, 40 40, 30, 20, 10, 6, 3, –3, –6, –8, –10 2500 测试集 25, 30, 40 40, 30, 20, 10, 6, 3, –3, –6, –8, –10 2500
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表 4 有无频漂时去掉CNN前后译码性能
迭代次数 字准确率(%) 词准确率(%) 有CNN，无频漂 23 99.92 99.65 无CNN，无频漂 42 92.71 73.90 有CNN，有频漂 26 96.23 91.71 无CNN，有频漂 47 63.11 20.35
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