Bidirectional Stackable Recurrent Generative Adversarial Imputation Network for Specific Emitter Missing Data Imputation
Haozhe Li, Yilin Liao, Zijian Tian, Zhaoran Liu, Jiaqi Liu, Xinggao Liu
Abstract
Specific emitter identification (SEI) uses the electromagnetic pulse signal sent by emitter to determine the emitter individual. In the actual complex electromagnetic environment, due to the interference of external signals and hardware failures, it is difficult to obtain sufficient and complete transmitter signal data. The missing data imputation methods are used to impute the emitter signal data. However, the existing imputation methods need to rely on the complete signal data to train the deep learning model, and the imputation error is large due to the long sequence characteristics of the signal. Therefore, a new specific emitter missing data imputation model is proposed, which is called bidirectional stackable recurrent generative adversarial imputation network (BiSRGAIN) including a generator and a discriminator. Specifically, the bidirectional stackable recurrent (BiSR) unit is designed to be used in generators and discriminators, which simplifies the traditional recurrent neural network (RNN) structure and improves parameter utilization and inference efficiency. The novel loss function can make the training of the model independent of the true value of the missing components, so the model can be trained in incomplete data. Extensive experiments are conducted on real-world dataset. The results show that the proposed model has lower errors under the scenario of high missing rate. In addition, the proposed model has higher parameter utilization and computational efficiency. Moreover, the completed signal data after imputation is used to identify specific emitters, and the results show that the data obtained by BiSRGAIN can achieve higher recognition accuracy.