Specific Emitter Identification Based on Joint Variational Mode Decomposition

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, it has not been well investigated in noisy SEI scenarios. Specifically, the existing VMD algorithms do not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, nor do they consider the vulnerability of this distortion estimates to channel noise, both of which constrain its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition and channel noise estimation on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters. In addition, channel noise is estimated during signal decomposition, which improves the ability to stably extract these distinctive characteristics. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with other SEI schemes, the JVMD-based scheme improves the accuracy of device classification at low SNRs.