A Hybrid Model With Bayesian Nonparametric Inference for RF Fingerprint Identification

Radio frequency fingerprint identification (RFFI) aims to identify subtle impairments in hardware devices, which play an important role in the mobile environment security community. To identify various mobile devices in the complex electromagnetic environment, deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN) have been adopted to extract device hardware-related features. However, the single network structure has difficulty in comprehensive feature extraction, as many factors can introduce hardware impairments. In this paper, we propose a hybrid model termed switching dynamical deep network (SDDN) for RFFI tasks, which can jointly extract both coarse-grained radio frequency fingerprints (RFFs) and fine-grained RFFs. Additionally, the proposed hybrid model consists of a probabilistic part and a deterministic part. Specifically, in the probabilistic part, the switching linear dynamical systems (SLDS) are incorporated to establish the correspondence between the signal slice and the feature extraction network (FEN). In the deterministic part, multiple independent FENs are established to extract the RFFs. Moreover, to automatically determine the suitable number of FENs, a Bayesian nonparametric prior distribution is placed over the probabilistic part. Finally, an end-to-end parameter optimization method that is based on variational inference and stochastic gradient descent is proposed. Experiments on a real-life Wi-Fi dataset demonstrate the superiority of the proposed method over existing methods.