# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 1 Introduction  
Specific emitter identification (SEI) refers to the process of identifying different emitters by analyzing the unique features of received radio signals [1]. In recent years, SEI has been widely used in both civilian and military fields. In cognitive radio systems, SEI can be used to verify the identity of primary users in a bid to prevent secondary users from occupying the licensed portion of the spectrum for a long time or to prevent disguisers from maliciously accessing the available spectrum [2]. In real combat environments, SEI can be used to identify enemies and friends, recognize interference sources, and provide battlefield situational awareness information [3]. Therefore, developing an advanced SEI technology is beneficial for us to further monitor and manage the interested targets. For space-specific emitters, radio-frequency fingerprints (RFFs) can be extracted from their downlink signals and transmitted to the ground in a bid to achieve SEI. These RFFsoriginate from the nonlinear distortion of modulated signals caused by the influence of high-power amplifiers carried by satellite transponders [4]. RFFs are unique and cannot be replicated due to the inability of the modern manufacturing processes to accurately match two power amplifiers [5]. RFFs can usually be obtained through three methods. The first method is to directly extract RFFs from the modulated signal. [PERSON]. [6; 7] used I/Q amplitude and phase imbalance as an example to study the accurate identification problem of different transmitters. Ref. [8] achieved physical layer hardware authentication by extracting I/Q constellation impairments. Ref. [9] proposed a novel method for the blind estimation of carrier frequency offset (CFO) in MPSK receivers, and CFO was used as an RFF to identify specific emitters. The second method is to obtain RFFs by performing various transformations on the modulated signal. Ref. [10] considered the SEI problem in both single-hop and relaying scenarios, as well as three RFF extraction algorithms based on the Hilbert spectrum were proposed. Ref. [11] studied an SEI method based on the variational mode decomposition and spectral features (VMD-SF). VMD decomposes the received signal simultaneously into various temporal and spectral modes. Different spectral features were extracted and achieved satisfactory results in SEI. Ref. [12] subjected the received signals to time-varying filtered empirical mode decomposition (tvf-EMD). Thereafter, the amplitude-frequency aggregation characteristics of the three-dimensional Hilbert spectrum projection and the bispectrum diagonal slice of the obtained intrinsic mode functions were used as the first and second features of SEI, respectively. Ref. [13] proposed a novel nonlinear dynamics approach based on multi-dimension approximate entropy (MApEn) for SEI. The RFFs extracted via this method could also achieve satisfactory results when using the simplest K-nearest neighbors classifier. Ref. [14] developed an SEI algorithm via joint wavelet packet analysis. The algorithm decomposes the signal via wavelet packet decomposition and extracts features of singular value center of gravity, instantaneous frequency distribution, and information demission. A support vector machine based on a voting mechanism was used to identify different emitters. However, both direct extraction and domain transformation require a careful design of RFFs and classifiers, which considerably increases the complexity of the task [15]. Additionally, the RFFs extracted using the above-mentioned two methods are susceptible to noise, which considerably fluctuates the recognition accuracy [16]. The third method is to use intelligent recognition technology to automatically extract RFFs from the received signal and complete end-to-end classification. Deep learning (DL) can compensate for the problems of the first two methods.