); [EMAIL_ADDRESS] (P.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (S.Z.) Can Xu  
Graduate School, Space Engineering University, Beijing 101416, China; [EMAIL_ADDRESS] (J.H. ); [EMAIL_ADDRESS] (C.X. ); [EMAIL_ADDRESS] (P.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (S.Z.) Can Xu  
Graduate School, Space Engineering University, Beijing 101416, China; hjl997@stu. is a pressing issue that needs to be addressed. Accurately identifying the identity of the target communication satellite is a key step in ensuring the efficiency of space-ground communication [5]. To achieve the crucial step, some scholars have proposed applying specific emitter identification (SEI) technology to the efficient and accurate identification of communication satellites. Specific emitter identification [6], also known as emitter individual identification or radio frequency fingerprint (RFF) identification, which directly extracts RFF features from RF signals that are difficult to be imitated and cloned for identifying different emitters, helps to improve the security of the authentication system. The RFF features of each emitter is inherent and unique, as it is derived from the imperfection of the emitter's hardware [7]. SEI can be mainly categorized into identification methods based on the transient signal and the steady-state signal [8]. The transient signal is generally generated during the transmitter turning on or off and has a short duration. However, the steady-state signal appears during the regular operation of the transmitter, features a longer duration, and is easier to capture compared to the transient signal. Thus, SEI based on the steady-state signal is the main research direction at present [9]. In summary, the target satellite can be uniquely identified with the help of SEI technology in order to enhance the management efficiency of satellite communication systems. Identification of the individual satellite still faces many challenges in practical applications. Due to dynamic changes in weather, equipment location, and transmission channels, it is not possible to maintain stable and good quality for a long time during receiving the RF signals of satellites on the ground. The RF signals we received even over a short continuous period of time usually have various SNRs [10]; thus, there is an urgent need for the robust SEI system of the satellites which can adapt to the dynamic noisy environment and does not require a good quality dataset. Recently, deep learning (DL) has been introduced into SEI technology for improving identification performance [11]. Deep learning methods are driven by signal data and extract specific RFF features from signals for identification, thereby achieving higher identification accuracy than traditional feature engineering methods. However, most of the general deep learning methods split complex signals into in-phase (I) and quadrature (Q) components before processing due to the fact that real-valued neural networks (RVNNs) cannot directly process complex signals. The split preprocessing ignored the correlation between the in-phase and quadrature components of complex signals, which result in the degradation of identification performance [12]. Inspired by the widespread application of generative adversarial networks (GANs) in the field of image denoising [13], we proposed a satellite individual identification method based on complex-valued conditional generative adversarial networks (CC-GANs). The method aims to address the high difficulty of signal preprocessing and the low quality of the RF signal dataset of the SEI system in the task of identifying the identity of the target satellite in a dynamic noisy environment. The SEI system we designed for communication satellites consists of a signal acquisition module, a signal preprocessing module, and an RFF features extraction and classification network adapted to dynamic noisy signals (CC-GANs), as shown in Figure 1. The main steps of the system are as follows:  
1.