# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 1 Introduction
In recent years, with the mature application of DL in fields such as computer vision and natural language processing, SEI methods based on DL have also been proposed one after another. Many research works showed that DL-based SEI performs considerably better than traditional methods [17]. [PERSON]. [18] used the same inception-residual neural network structure for large-scale real-world ACARS and ADS-B signal data. The authors confirmed the ability of DL to address different types of radio signals. The classification accuracy on the two datasets exceeded 92% when the signal-to-noise ratio (SNR) was higher than 9 dB. [PERSON]. [19] proposed a multisampling convolutional neural network (MSCNN) to extract RFFs from 54 ZigBee devices. MSCNN automatically uses multiple down-sampling transformations for multiscale feature extraction and classification. The classification accuracy is as high as 97% under the line-of-sight scenario with SNR = 30 dB. [PERSON]. [20] used a convolutional neural network and compressed the bispectrum of received signals to identify specific emitters. This method can be used to extract overall feature information hidden in the original signals. [PERSON]. [21] developed an efficient SEI method based on complex-valued neural networks (CVNNs) and network compression. CVNNs are used to enhance the recognition effect of specific emitters. Network compression ensures satisfactory recognition results while reducing the model's complexity. [PERSON]. [22] supposed that the existing methods only consider the feature of signals or the feature after signal transformation. They ignored the temporal correlation of each feature and the relationship between the features. Therefore, a model named time-domain graph tensor attention network (TDGTAN) was proposed for SEI. This model offers higher accuracy and anti-interference performances for real-worlddatasets. Table 1 discusses the mentioned literature. Additionally, some SEI methods based on DL are also used to solve more detailed practical problems such as few-shot SEI [23; 24], unsupervised SEI [25; 26], semi-supervised SEI [27; 28], open-set SEI [29; 30], and malicious attack recognition [31; 32]. Although SEI methods based on DL have been applied to real-world data, no effective solutions have been proposed for the long time span SEI problem. To our best knowledge, the vast majority of SEI works currently use data with the same distribution. Conversely, they all use data collected from the same batch. However, the more realistic situation is that the testing set often lags behind the training set in time, and its data distribution dynamically changes. Therefore, models trained using the training set may poorly perform on the testing set. Fine-tuning may be an effective method to solve the long time span SEI problem [33]. It involves freezing some parameters of the pretrained model and adjusting the deep layers of the network to meet the training and testing requirements of new data. However, the labels of the actual testing set are often unknown. The above-mentioned issues pose greater challenges to the practicality of DL-based SEI methods. Only limited research has been performed in the past on solving the long time span SEI problem: Ref. [34] developed an adaptive SEI system for the dynamic noise domain. The authors proposed a preprocessing algorithm called improving synchrosqueezed wavelet transforms by energy regularization, and an unsupervised neural network noise feature extracting GAN (NEGAN) (note that \"GAN\" means generative adversarial network). NEGAN can obtain clean RFFs from noisy signals and reduce dependence on dataset quality. However, the proposed signal preprocessing algorithm and NEGAN are relatively complex. Ref. [35] considered that the actual application environment is more complex than the ideal training environment and, therefore, developed an unsupervised domain adaptive-based modulation classification for overlapped signals.