Few-Shot Specific Emitter Identification Method Based on Lossless VMD and Mamba

HE Wenlong; CHEN Jian; YIN Baoxin; MAO Haoyang; JIA Jie; WANG Xingwei

doi:10.12263/DZXB.20260405

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Few-Shot Specific Emitter Identification Method Based on Lossless VMD and Mamba

RF Computing | 更新时间：2026-10-16

- Few-Shot Specific Emitter Identification Method Based on Lossless VMD and Mamba
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2509-2521(2026)
- 作者机构：
  
  东北大学计算机科学与工程学院，辽宁沈阳 110169
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62132004;62572109;62572113);Aeronautical Science Foundation of China(20230026050001);Central Government Guided Local Science and Technology Development Fund Project of LiaoNing(2025JH6/101100009);Young and Middle-Aged Leading Talents in Technological Innovation of Shenyang(RC231173);Shenyang Science and Technology Plan Fund Project(23-503-6-17)
- DOI：10.12263/DZXB.20260405
  CLC： TN911.7;
- Received：26 April 2026，
  
  Accepted：16 May 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
何文龙, 陈剑, 尹宝鑫, 等. 基于无损VMD和Mamba的小样本辐射源识别方法[J]. 电子学报, 2026, 54(06): 2509-2521.

HE Wenlong, CHEN Jian, YIN Baoxin, et al. Few-Shot Specific Emitter Identification Method Based on Lossless VMD and Mamba[J]. Acta Electronica Sinica, 2026, 54(06): 2509-2521.
何文龙, 陈剑, 尹宝鑫, 等. 基于无损VMD和Mamba的小样本辐射源识别方法[J]. 电子学报, 2026, 54(06): 2509-2521. DOI：10.12263/DZXB.20260405

HE Wenlong, CHEN Jian, YIN Baoxin, et al. Few-Shot Specific Emitter Identification Method Based on Lossless VMD and Mamba[J]. Acta Electronica Sinica, 2026, 54(06): 2509-2521. DOI：10.12263/DZXB.20260405

摘要

随着无线通信技术的快速发展，无线通信辐射源数量呈现出指数级增长态势，电磁环境日益复杂，频谱资源日趋紧张。在这一背景下，如何在海量的设备中实现精准的身份识别与安全接入，已成为关乎社会稳定与经济发展的核心议题。传统的身份认证方式依赖协议层或应用层的数字凭证，存在易被窃取、伪造或攻击等安全漏洞。相比之下，基于射频指纹（Radio Frequency Fingerprinting，RFF）的特定辐射源识别技术（Specific Emitter Identification，SEI）作为一种物理层的安全手段，具有广阔应用前景。该技术通过提取发射机硬件电路中固有的物理缺陷进行身份认证，具有天然的唯一性与长期稳定性。然而，现有基于深度学习的通信辐射源识别方法普遍面临对标注数据依赖性强、泛化能力不足、长序列信号处理效率低等的挑战。为此，本文重点聚焦小样本条件下通信辐射源的高效识别问题，提出了一种基于自监督学习的低复杂度识别算法。首先，针对原始I/Q信号中指纹特征易被信道噪声和调制信息掩盖的问题，本文引入了无损的变分模态分解（Variational Mode Decomposition，VMD）方法，通过自适应地将原始I/Q信号转换为多个本征模态函数分量，增强了指纹特征的表示能力；其次，为克服传统序列建模模型在处理长序列信号时的二次计算复杂度瓶颈，本文引入了具有线性计算复杂度的Mamba架构，并构建了一个基于Mamba线性流编码器的自监督网络模型，利用大规模辅助数据集进行预训练，结合对比学习和监督学习，促使模型学习具有强判别性的通用特征表示；最后，针对实际应用中新增设备样本稀缺和存在未知干扰的问题，本文采用了迁移学习策略，利用少量的目标域辐射源样本微调一个轻量级线性分类器，以快速适应具体的下游分类任务。在WiSig数据集上的仿真实验结果表明，本文所提出的方法在6-way 1-shot场景下识别准确率达到91.20%，在显著降低了计算复杂度与样本集规模依赖的同时保持了较高的识别性能，具有良好的现实应用前景。

Abstract

With the rapid development of wireless communication technology

the number of wireless communication emitters has grown exponentially

leading to an increasingly complex electromagnetic environment and increasingly scarce spectrum resources. Against this backdrop

achieving precise identity recognition and secure access among massive devices has become a critical issue concerning social stability and economic development. Traditional identity authentication methods rely on digital credentials at the protocol or application layer

which are susceptible to theft

forgery

or attacks. In contrast

the specific emitter identification (SEI) technology based on radio frequency fingerprinting (RFF) serves as a physical-layer security measure with broad application prospects. This technology authenticates identities by extracting the inherent physical imperfections in the emitter’s hardware circuits

offering natural uniqueness and long-term stability. However

existing deep learning-based methods for communication emitter identification generally face challenges

including heavy reliance on labeled data

limited generalization

and low efficiency in processing long-sequence signals. To address these issues

this paper focuses on the efficient identification of communication emitters under few-shot conditions and proposes a low-complexity self-supervised recognition algorithm. First

to overcome the problem that fingerprint features in the original I/Q signals are easily obscured by channel noise and modulation information

this paper introduces a lossless variational mode decomposition (VMD) method. This approach adaptively converts the original I/Q signals into multiple intrinsic mode function components

enhancing the representational capacity of fingerprint features. Second

to address the quadratic computational complexity bottleneck of traditional sequence modeling methods when processing long-sequence signals

this paper incorporates the Mamba architecture

which has linear computational complexity. A self-supervised network model based on the Mamba linear flow encoder is constructed

utilizing a large-scale auxiliary dataset for pre-training. By integrating contrastive and supervised learning

the model is guided to learn discriminative

generalizable feature representations. Finally

to tackle the scarcity of new device samples and the presence of unknown interference in practical applications

this paper employs a transfer learning strategy. Using a small number of target-domain emitter samples

a lightweight linear classifier is fine-tuned to adapt to specific downstream classification tasks quickly. Simulation results on the WiSig dataset show that the proposed method achieves 91.20% accuracy in a 6-way 1-shot scenario. While significantly reducing computational complexity and dependence on sample size

the method maintains high recognition performance

demonstrating promising potential for real-world applications.

关键词

Keywords

references

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