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specific emitter identification for IoT Devices Based on Deep Residual Shrinkage Networks

China Communications 2021年第12期18卷 81-93页

作者： Peng Tang Yitao Xu Guofeng Wei Yang Yang Chao Yue College of Communications Engineering Army Engineering University of PLANanjing 210007China

specific emitter identification can distin-guish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits.Feature extraction is a key part of traditional machine learning-based methods,but manual extrac-tion is generally limited by prior professional knowl-edge.At the same time,it has been noted that the per-formance of most specific emitter identification meth-ods degrades in the low signal-to-noise ratio(SNR)environments.The deep residual shrinkage network(DRSN)is proposed for specific emitter identification,particularly in the low SNRs.The soft threshold can preserve more key features for the improvement of performance,and an identity shortcut can speed up the training process.We collect signals via the receiver to create a dataset in the actual environments.The DRSN is trained to automatically extract features and imple-ment the classification of transmitters.Experimental results show that DRSN obtains the best accuracy un-der different SNRs and has less running time,which demonstrates the effectiveness of DRSN in identify-ing specific emitters.

关键词： specific emitter identification IoT de-vices deep learning soft threshold deep residual shrinkage networks

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specific emitter identification via Squeeze-and-Excitation Neural Network in Frequency Domain

Specific Emitter Identification via Squeeze-and-Excitation N...

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第40届中国控制会议

作者： Yinan Peng Yuan Zhou Tianjin University

specific emitter identification（SEI） is a technology that identifies the emitter individual using the characteristic of their transmit signal.SEI has been widely adopted in military and civilian use.As for SEI task,in time domain,general neural network has a poor feature extraction ability that usually gets a low recognition accuracy in a low signal-to-noise ratio（SNR）environment.So,we propose a SEI method based on squeeze-and-excitation neural network（SeNet） in frequency *** uses a squeeze-and-excitation module to achieve channel attention mechanism,so the neural network can emphasize the useful feature and weaken the useless feature in different channels.The experiment result shows that,in frequency domain,SeNet has better emitter identification ability and stronger robustness than the general neural network even in a low SNR environment.

关键词： specific emitter identification frequency domain squeeze-and-excitation attention

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