  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, the trade-off of VMD between the robustness to
noise and the ability to preserve signal information has not been investigated
in the current literature. Moreover, the existing VMD algorithm does not
utilize the stability of the intrinsic distortion of emitters within a certain
temporal span, consequently constraining 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 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 and reduces noise. 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 VMD, the JVMD
algorithm improves the accuracy of device classification and the robustness
towards noise.
