基于改进CLDNN的辐射源信号识别

传统辐射源信号识别方法往往需要人工提取特征,不仅对专业知识要求较高,而且人为选择的特征不能够保证适用于大多数类型信号的识别,识别精度和识别速度也不能兼顾。针对上述问题,将语音处理领域常用的深度学习模型——卷积长短时深度神经网络(convolutional long short-term deep neural network, CLDNN)引入到辐射源信号的识别中,并将该模型中的长短时记忆层改为双向门控循环单元层。模型的输入为原始时间序列数据,特征提取和分类识别过程均在网络中进行,避免了人工选择特征的不完备性。实验结果表明,所提模型在低信噪比情况下也能够有效识别信号类型,同时与其他模型相比,实现了识别精度和识别速度之间的平衡。

Emitter signal recognition based on improved CLDNN

Traditional methods of emitter signal recognition often need to extract features manually, which not only requires high professional knowledge, but also can not guarantee that the features selected by humans are suitable for the recognition of most types of signals, meanwhile, the recognition accuracy and speed cannot be taken into account. To solve the above problems, convolutional long short-term deep neural network(CLDNN), a deep learning model commonly used in speech processing, is introduced into the recognition of emitter signal, and the long short-term memory (LSTM) layer in this model is changed into bidirectional gated recurrent unit (Bi-GRU) layer. The input of the model is the original time series data, and the processes of feature extraction and classification recognition are carried out in the network to avoid the incompleteness of artificial feature selection. Experimental results show that the proposed model can recognize the signal types effectively at low signal to noise ratio, and a balance between recognition accuracy and recognition speed is achieved compared with other models.