Abstract Aiming at the difficulty in radio fingerprint extraction caused by insufficient traditional training methods with small labeled samples, a deep autoencoder regularized by marginal Fisher analysis algorithm for radio fingerprint extraction is proposed. Based on deep autoencoder, the training procedure is divided into two parts, unsupervised pre-training and supervised finetuning based on marginal Fisher analysis. Firstly, the radio individual class information contained in the large amount of unlabeled samples is extracted. And the information is sent to the deep autoencoder for parameters optimization. Then, the trainable parameters are analyzed on the basis of marginal Fisher method with the assistant of labeled samples to improve the discriminant capability of fingerprint feature between radio individuals of the same model. The classification experiment is conducted on several communication radio signal datasets. The results show that the difference of radio individuals of the same model can be represented effectively by the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China(No.61272333), National Defense Technology Key Laboratory Fund(No.9140C130502140C13068), General Equipment Department Pre-Developed Project Fund(No.9140A33030114JB39470)
About author:: 黄健航(通讯作者),男,1994年生,硕士研究生,主要研究方向为通信对抗侦察.E-mail:rafael211@163.com. 雷迎科,男,1975年生,博士,副教授,主要研究方向为通信信号处理.E-mail:leiyingke@163.com.
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