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.

翻译：特定辐射源识别（SEI）技术在自组织组网和频谱管理等设备管控场景中具有重要安全意义，因此备受关注。针对非线性非平稳电磁信号，SEI 常采用变分模态分解（VMD）对信号进行分解，以有效表征设备的独特指纹特征。然而，当前文献尚未研究 VMD 在噪声鲁棒性与信号信息保留能力之间的权衡问题。此外，现有 VMD 算法未利用辐射源在一定时间跨度内固有畸变的稳定性，这限制了其在 SEI 中的实际应用。本文提出一种联合变分模态分解（JVMD）算法，该算法通过同时对多帧信号进行模态分解，是 VMD 的改进版本。该算法充分利用多帧信号在中心频率和固有模态函数（IMF）上的一致性，有效凸显辐射源间的差异特征并降低噪声。同时，本文分析了 JVMD 的复杂度，证明其计算效率优于 VMD。基于真实数据集的模态分解和 SEI 仿真结果表明，与 VMD 相比，JVMD 算法提升了设备分类精度和噪声鲁棒性。