Aimed at the problem that individual identification of communication radiation sources has a cer-tain lack of label data under conditions of non-cooperative communication,a method of individual identifi-cation of communication emitter is proposed based on deep clustering.The powerful feature extraction and data reconstruction capabilities of the auto-encoder network are utilized for carrying out the representation learning of the original I/Q data,extracting the fingerprint features of individual recognition,and jointly optimizing the representation learning process and the feature clustering process,so as to achieve a higher fit between the representation learning and the feature clustering,and complete still greater individual i-dentification of the communication emitter without labels.The experimental results show that the recogni-tion accuracy is more than 85%when the SNR is above 0 dB.And the proposed method is valid and sta-ble.
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