Robust Open Set Specific Emitter Identification Using Reciprocal Points Learning and Deep Reconstruction Learning

In smart wireless communication environments, specific emitter identification (SEI) technology has become a crucial means to ensure the security and stability of the wireless communication system. With the rapid increase in the number of Internet of Things (IoT) devices, traditional closed-set identification methods are no longer adequate to handle dynamic and complex wireless environments, particularly for unknown and rogue device intrusions. Consequently, open set SEI (OS-SEI) methods have emerged, which not only identify known devices but also effectively detect previously unseen rogue devices, thereby providing enhanced security and reliability. Therefore, this paper proposes an OS-SEI method based on reciprocal points learning and deep reconstruction learning (RPDRL). Firstly, by introducing an attention-based convolutional autoencoder (ACAE) with skip-layer connections (SC), which is used for deep reconstruction learning, along with reciprocal points learning (RPL), the extracted features become more robust. Furthermore, we design a classification algorithm that combines an appropriate fingerprint metric and extreme value theory (EVT), effectively achieving the detection of rogue devices and the classification of known devices. An open-source automatic dependent surveillance-broadcast (ADS-B) dataset and an intercom dataset are used to evaluate the RPDRL-based OS-SEI method. Experimental results indicate that the proposed method achieves an accuracy of 94.88% on the ADS-B dataset and 96.00% on the intercom dataset. Ablation experiments demonstrate the effectiveness of the efficient channel attention (ECA) modules and SC in the proposed network structure, as well as the efficacy of each loss function.