扩散模型驱动的跨时间域通信辐射源个体增量识别方法

赵东兴; 刘辉; 黄科举; 杨俊安

doi:10.12263/DZXB.20250843

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扩散模型驱动的跨时间域通信辐射源个体增量识别方法

学术论文 | 更新时间：2026-04-24

- 扩散模型驱动的跨时间域通信辐射源个体增量识别方法
- A Diffusion Model Driven Approach for Cross-Time-Domain Incremental Specific Emitter Identification
- 电子学报 2025年53卷第12期页码：4527-4540
- 作者机构：
  
  1.国防科技大学电子对抗学院电子对抗工程系，安徽合肥 230000
  2.安徽省电子制约技术重点实验室，安徽合肥 230000
- 作者简介：
  
  [ "赵东兴男，1997年9月出生于浙江省义乌市.现为国防科技大学电子对抗学院博士研究生.主要研究方向为通信辐射源个体识别、增量学习、扩散模型及信号处理.E-mail: zhaodongxing17@nudt.edu.cn" ]
  [ "刘辉男，1983年11月出生于安徽省阜阳市.2011年毕业于原解放军电子工程学院.现为国防科技大学副教授、硕士生导师.主要研究方向为通信对抗、智能信息处理.E-mail: liuhui17c@nudt.edu.cn" ]
  [ "黄科举男，1994年4月出生于山西省运城市.2022年毕业于国防科技大学电子对抗学院.现为国防科技大学讲师.主要研究方向为智能信号处理.E-mail: huangkeju@nudt.edu.cn" ]
  [ "杨俊安男，1965年10月出生于安徽省巢湖市.2003年毕业于中国科学技术大学.现为国防科技大学教授、博士生导师.主要研究方向为通信对抗、智能信息处理.E-mail: yangjunan17@nudt.edu.cn" ]
- 基金信息：
  
  国防科技大学青年自主创新科学基金(ZK24-44)
- DOI：10.12263/DZXB.20250843
  中图分类号： TN911.7;
- 收稿：2025-09-25，
  
  录用：2025-12-22，
  
  纸质出版：2025-12-25
- 稿件说明：
移动端阅览
赵东兴, 刘辉, 黄科举, 等. 扩散模型驱动的跨时间域通信辐射源个体增量识别方法[J]. 电子学报, 2025, 53(12): 4527-4540.

ZHAO Dong-xing, LIU Hui, HUANG Ke-ju, et al. A Diffusion Model Driven Approach for Cross-Time-Domain Incremental Specific Emitter Identification[J]. Acta Electronica Sinica, 2025, 53(12): 4527-4540.
赵东兴, 刘辉, 黄科举, 等. 扩散模型驱动的跨时间域通信辐射源个体增量识别方法[J]. 电子学报, 2025, 53(12): 4527-4540. DOI：10.12263/DZXB.20250843

ZHAO Dong-xing, LIU Hui, HUANG Ke-ju, et al. A Diffusion Model Driven Approach for Cross-Time-Domain Incremental Specific Emitter Identification[J]. Acta Electronica Sinica, 2025, 53(12): 4527-4540. DOI：10.12263/DZXB.20250843

摘要

通信辐射源个体识别（Specific Emitter Identification，SEI）利用信号发射设备由于制造误差与器件老化等因素形成的细微硬件差异，在物理层完成身份区分与溯源.相较依赖协议与密钥的传统认证方式，它无需改动协议栈，对业务内容透明且部署成本低，已在频谱监管、无线安全、认知无线电与复杂电磁环境感知等场景展现重要价值.然而在真实无线环境中，跨时间与跨场景的信道变化会对射频指纹产生不稳定的调制与失真：多径衰落、频偏与相位噪声随时间漂移，使同一设备在不同时刻的信号表现差异显著，识别性能在目标域显著退化，成为落地应用的核心障碍.为缓解域间分布偏移，现有研究主要从迁移学习与领域自适应展开.迁移学习依赖目标域标注进行微调，虽可提升目标域性能，但易破坏源域已学知识并诱发灾难性遗忘；无监督领域自适应通过特征对齐、伪标签与熵最小化缩小分布差异，但因缺乏显式监督，提升幅度受限，且难以应对数据持续到来的在线场景.增量学习强调在不断接收新数据时兼顾“适应—记忆”的平衡，但多数方法仍需标注或额外存储，难以直接用于无线信号的无标注跨时间应用.生成式建模的发展为解决该问题提供了新契机.扩散模型以正向加噪、反向去噪的机制刻画复杂分布，具备建模“信道扰动—设备本征特征”叠加关系、从观测中恢复指纹的潜力，但既有工作多聚焦去噪或数据生成，尚未兼顾跨时间域识别与持续学习需求.为此，本文提出一种扩散模型驱动的跨时间域通信辐射源个体增量识别方法：在源域阶段以正向扩散显式建模信道扰动，在目标域通过反向扩散逐步恢复接近源域分布的判别性表示，以抑制特征漂移；在扩散网络中引入跨注意力，将个体信息注入去噪过程以增强类间可分性；进一步结合无监督增量学习，通过分布一致性与知识保持正则，仅依赖无标注目标样本实现持续自适应，并有效缓解遗忘.在WiSig数据集上，跨时间个体识别实验表明，所提方法在目标域识别准确率较典型领域自适应方法提升超过5个百分点，在源域性能保持方面较主流增量学习策略提升约10个百分点，验证了其信道还原与特征对齐能力，并体现出在动态信道条件下的鲁棒性与实用价值.

Abstract

Specific emitter identification (SEI) exploits subtle hardware discrepancies caused by manufacturing imperfections and device aging to perform transmitter identification and attribution at the physical layer. Compared with traditional authentication schemes that rely on protocols and cryptographic keys

SEI requires no modification to the protocol stack

is transparent to transmitted data

and incurs low deployment cost

making it valuable for applications such as spectrum regulation

wireless security

cognitive radio

and sensing in complex electromagnetic environments. However

in real-world wireless scenarios

time-varying and scene-dependent channel conditions introduce unstable modulation and distortion to radio-frequency fingerprints. Effects such as multipath fading

carrier frequency offset

and phase noise drift over time

causing the signals emitted by the same device to exhibit significant temporal variation. As a result

identification performance degrades markedly in the target domain

posing a major obstacle to practical deployment. To mitigate domain distribution shifts

existing studies mainly investigate transfer learning and domain adaptation approaches. Transfer learning relies on fine-tuning with labeled target-domain data and can improve target-domain performance

but it often disrupts previously learned source-domain knowledge and leads to catastrophic forgetting. Unsupervised domain adaptation reduces distribution discrepancies through feature alignment

pseudo labeling

and entropy minimization; however

due to the absence of explicit supervision

performance improvements are limited

and such methods struggle to handle continuously arriving data in online scenarios. Incremental learning emphasizes balancing adaptation to new data with the preservation of prior knowledge

yet most existing approaches still require labeled data or additional storage

making them difficult to apply directly to unlabeled cross-time SEI tasks. The advancement of generative modeling provides a new opportunity to address these challenges. Diffusion models characterize complex data distributions through forward noise injection and reverse denoising processes

and are well suited for modeling the superposition of channel perturbations and device-intrinsic features

enabling the recovery of radio-frequency fingerprints from distorted observations. Nevertheless

existing studies predominantly focus on denoising or data generation

and have not fully addressed cross-time identification and continual learning requirements. To this end

this paper proposes a diffusion-model-driven cross-time incremental SEI method. In the source domain

forward diffusion is employed to explicitly model channel perturbations

while in the target domain

reverse diffusion progressively restores discriminative representations that approximate the source-domain distribution

thereby suppressing feature drift. A cross-attention mechanism is incorporated into the diffusion network to inject emitter identity information during denoising

enhancing inter-class separability. Furthermore

an unsupervised incremental learning strategy is introduced

which achieves continual adaptation using only unlabeled target-domain samples through distribution consistency and knowledge-preservation regularization

effectively mitigating catastrophic forgetting. Cross-time identification experiments on the WiSig dataset demonstrate that the proposed method improves target-domain identification accuracy by more than 5 percentage points compared with representative domain adaptation methods

and enhances source-domain performance retention by approximately 10 percentage points relative to mainstream incremental learning strategies

validating its channel restoration capability

feature alignment effectiveness

and robustness under dynamic channel conditions.

关键词

Keywords

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