基于深度神经网络的Morse码自动译码算法

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。

Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network

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.