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摘要: 特定辐射源个体识别依赖设备发射信号的“射频指纹”微弱差异识别辐射源所属个体身份属性，是支撑无线安全、频谱管控和态势感知的重要基石。随着无线场景的多样化与动态化，单一域(源域与目标域分布相同)下训练的深度学习模型往往在跨接收机、跨时间等真实环境中性能急剧下降，目前缺乏关于该方面全面、细致的综述。基于此，该文首先对跨场景类型进行分类，然后系统归纳总结主流算法框架及代表性方法，着重剖析每类方法的核心思想与关键技术，并总结了主要开源跨场景数据集，最后指出当前研究的瓶颈与未来可能方向，旨在推动复杂电磁环境下的辐射源个体识别理论和方法研究的新发展。
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关键词:
- 跨场景特定辐射源识别 /
- 射频指纹 /
- 深度学习 /
- 迁移学习 /
- 域适应
Abstract: Specific Emitter Identification (SEI) relies on subtle differences in the radio frequency fingerprints of device-emitted signals to determine the emitter identity attributes. SEI plays a fundamental role in wireless security, spectrum management, and situational awareness. However, as wireless scenarios become increasingly diverse and dynamic, deep learning models trained in a single domain (where the source and target domains share the same distribution) often suffer severe performance degradation in real-world settings such as cross-receiver and cross-time scenarios. This degradation has not yet been comprehensively analyzed. To address this issue, this paper first classifies SEI according to cross-scenario types, and then systematically reviews mainstream algorithm frameworks and representative SEI methods, with a particular focus on the core ideas and key technologies underlying each method. It also summarizes the main open-source cross-scenario SEI datasets. Finally, the paper identifies current research bottlenecks and outlines potential future directions, aiming to facilitate advances in SEI theories and methodologies applicable to complex electromagnetic environments. -
表 1 基于对抗式域对齐的跨单源域识别算法对比[26−35]
Table 1. Comparison of cross-single-domain identification algorithms based on adversarial domain alignment[26−35]
算法 核心工作 算法不足 跨场景类型 效果分析 ACR-SEI 结合对抗训练与一致性正则化
提升跨域特征稳定性依赖高质量未标注数据，对抗训练稳定性待提升 跨接收机、
空间场景在ORACLE射频指纹数据集中准确率最高99% (12 dB环境)[26] DADA 采用对抗生成网络对齐源域与
目标域特征分布对目标域数据分布差异敏感，对抗训练耗时长 跨空间场景 在自建数据集中准确率最高99.28%
(25 dB环境)[27]UAV-DANN 结合Transformer和DANN联合
对齐信号分布不适应低信噪比场景，泛化能力受限 跨空间场景 在自建无人机遥控数据集上准确率最高88.35%[28] SSDA-AMR 联合半监督学习与域不变特征提取，解决调制类型变化问题 需部分目标域标注，复杂调制场景性能下降 跨调制场景 在RadioML2016.10A, RadioML2018.01A和自定义数据集上准确率可超过90% (0 dB环境)[29] OpenRF 基于真实5G平台实现端到端
指纹认证依赖特定硬件环境，泛化能力受限 跨空间、
协议场景在POWDER平台自建数据集上准确率99.86%[30] FRFDA 联邦框架下实现多源模型参数迁移，保护隐私 通信开销大，异构设备
兼容性差跨空间场景 在POWDER平台自建数据集上准确率90.33% (4G, 5G, Wi-Fi混合数据)[31] DA-VMSEI 自适应调制参数估计与域对齐
联合优化对调制参数突变适应性不足 跨调制场景 在自建数据集上准确率85%
(4 dB环境)[32]DADF 利用深度神经网络从原始信号中提取鲁棒且可区分的设备指纹特征 在极端或未见过的领域条件下，泛化能力会下降 跨空间、
功率场景在自建数据集上准确率最高99.7%[33] DDA-DN 引入动态噪声补偿模块，结合对抗性域适应实现变环境下的稳定识别 对噪声统计特性突变敏感，
需预先建模噪声分布跨空间场景 在自建数据集上准确率82%[34]
(6 dB环境)CBAM-CNN-BDA 融合通道注意力机制与双向域对抗网络，增强指纹的细粒度特征表达与
跨接收机适配能力注意力模块对接收机硬件参数敏感，需接收机端先验校准 跨接收机场景 在自建数据集上准确率97.5%[35] 
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表 2 基于分布度量对齐的跨单源域识别算法对比[36−41]
Table 2. Comparison of cross-single-domain identification algorithms based on distribution metric alignment[36−41]
算法 核心工作 算法不足 跨场景类型 效果分析 AMC-SS 联合对抗训练与多约束优化解决未标注样本特征漂移问题 对抗约束设计复杂，计算成本高 跨接收机场景 在自建数据集上准确率99%[36] DAN 提出通过多层MK-MMD对齐特征分布 对源域数据量依赖性强，
计算复杂度较高跨时间场景 使用公开数据集(Office-31, Office-Caltech, ImageCLEF-DA)最高平均准确率84.6%[37] JSA 构建跨接收机的联合子空间实现辐射源特征不变性表征 子空间维度敏感，
动态环境适应性弱跨接收机场景 在自建数据集上准确率超过85%[38] MSTL 基于子域粒度聚合多源知识，提升跨域泛化能力 子域划分依赖先验知识，异构源域兼容性差 跨频域场景 在自建数据集上准确率88%[39] DSBN 分离领域特有统计量，实现无监督域适应中特征分布解耦 需足够目标域批量样本，
域间差异过大时失效跨接收机场景 使用VisDA-C (平均80.2%), Office-31 (平均88.3%), Office-Home数据集
(平均83.0%)[40]MDAN 构建多级域差异度量模块，采用层次化特征对齐 计算复杂度高，
多差异权重需手动调整跨接收机、时间场景 在ORACLE数据集上识别率93.1%[41]
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Table 3. Comparison of cross-single-domain identification algorithms based on contrastive learning[42−46]
算法 核心工作 算法不足 跨场景类型 效果分析 CSSL-PL 结合对比学习与伪标签机制，增强在低标注率下的判别性特征学习 伪标签噪声抑制能力有限，
复杂调制场景鲁棒性下降跨调制、空间场景 在自建数据集上准确率超过85% (SNR大于–2 dB环境)[42] SSDF 基于自监督的双流特征编码器融合时频域信息，缓解小样本的
过拟合问题双流模型计算开销大，动态
场景特征同步性要求高跨接收机、空间场景 在自建数据集上准确率97.1%[43] CDA-PTN 结合原型网络和小样本学习，
能在无标注时实现高效的识别对域间分布差异过大的场景
泛化能力有限跨空间、接收机、
功率场景使用ORACLE, CORES, WiSig数据集，最高准确率99%[44] UCL-TS 引入时域偏移不变性约束的无监督对比学习框架 依赖高质量无标签时序数据，
短时突变适应性不足跨时间场景 在自建数据集上准确率88.2% (无线信道环境，时间跨1 d)[45] TPML 结合了伪标签和元学习机制，
可适应动态电磁场景依赖源域质量，伪标签的
准确性可能显著下降跨时间场景 在自建数据集上准确率90%[46]
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表 4 基于多源对抗式域对齐的跨多源域识别算法对比[47−49]
Table 4. Comparison of cross-multiple-domain identification algorithms based on multi-source adversarial domain alignment[47−49]
算法 核心工作 算法不足 跨场景类型 效果分析 PBDA 提出原型中心双对齐机制
(特征空间+原型分布对齐)对噪声和杂波敏感，
需高纯度原型初始化跨空间、
接收机场景使用ImageCLEF-DA (平均90.2%)、Office-31
(平均91.4%)、Office-Home (平均75.5%)
和自建数据集(超过88%)[47]ADAW 提出基于Wasserstein距离的
对抗域适应框架依赖大规模跨会话数据集，
计算复杂度高跨时间场景 ADS-B自建数据集中准确率最高86.78%[48] MFGADA 融合多分类器差异损失与梯度对齐策略，
提升了跨批次数据的特征对齐效果测试时间跨度小，
不适应实际场景跨时间场景 在自建数据集上平均准确率79.49%[49]
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表 5 基于分布度量对齐的跨多源域识别算法对比[50−52]
Table 5. Comparison of cross-multiple-domain identification algorithms based on distribution metric alignment[50−52]
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Table 6. Comparison of cross-multiple-domain identification algorithms based on contrastive learning[53,54]
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Table 7. Comparison of cross-multiple-domain identification algorithms based on relationship modeling[55,56]
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算法 核心工作 算法不足 跨场景类型 效果分析 CRDL-RF 利用多数据流融合增强信道鲁棒性，解决动态信道干扰下的设备指纹识别问题 计算复杂度高，依赖多模态数据同步采集 跨空间、协议场景 在自建数据集上准确率92%
(0 dB环境，相同瑞利信道
进行训练和测试)[57]AirID 通过注入定制化指纹提升无人机身份认证的独特性与抗伪造能力 需硬件生成特定畸变，
适配场景受限跨接收机、空间场景 在自建数据集上准确率98%[58] DeepFIR 自适应波形滤波抑制信道噪声，结合深度网络实现高鲁棒的物理层信号分类 实时性较差，滤波器参数
动态调整依赖离线训练跨空间、时间场景 在自建数据集上准确率74%
(10个滤波器头条件下)[59]RTAWS 实时生成抗干扰波形优化神经网络分类鲁棒性，支持动态信道反馈机制 依赖反馈链路延迟，
多用户场景资源竞争跨频域场景 使用DeepSig RADIOML 2018.01A和Wi-Fi信号自建数据集，在BPSK, 16QAM, 64QAM上识别准确率较对照组提升1.24倍、4.1倍、2.9倍[60] DACR-RFF 多模态数据增强以扩展RFF的
跨信道泛化能力存储与预处理成本高，增强数据可能引入分布偏移 跨接收机、空间、
时间场景使用美国DARPA提供的Wi-Fi数据集上准确率80%[61] LVNR-RFF 动态特征对齐模块支持可变长信号输入，抗噪声特征提取提升低信
噪比场景性能模型复杂度高，长序列推理延迟显著 跨空间场景 在自建数据集上，识别准确率67.26% (10 dB环境)[62]
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表 9 开源跨场景辐射源个体识别数据集[30,75,77−89]
Table 9. Open-source cross-scenario emitter individual identification datasets[30,75,77−89]
数据集 发射端 接收端 信号类型 跨场景方式 ORACLE[77] USRP X310 USRP B210 IEEE802.11a 跨空间场景 ShawabkaINFOCOM2020[75] USRP N210/X310 USRP N210 IEEE802.11a/g 跨时间、接收机场景 WiSig[78] Wi-Fi USRP B210/
N210/X310IEEE802.11a/g 跨时间、接收机场景 RFFP-dataset[79] Pycom IoT USRP B210 LoRa, Wi-Fi 跨时间、空间、接收机、通信协议场景 POWDER[30] USRP X310 USRP B210 5G, WiFi, LTE 跨时间、空间、接收机场景 RadarCommDataset[80] 多种仿真或实际雷达、通信设备 USRP N210 雷达、通信信号 跨空间、调制方式场景 AirID[81] SDR UAV SDR UAV/
SDR GroundIEEE802.11a 跨时间场景 Hovering UAVs[82] DJI M100 USRP X310 非标准波形 跨空间场景 LoRa-RFFI1[83] LoRa发射机 USRP LoRa 跨空间场景 LoRa-RFFI2[84] LoRa DUTs SDR接收机 LoRa 跨接收机场景 Bluetooth Data[85] Smartphone Tektronix
TDS7404Bluetooth 跨接收机场景 DroneRFa[86] UAVs USRP- 2955 无人机与遥控器通信信号 跨空间、频域场景 DroneRFb-DIR[87] UAVs USRP- 2955 无人机与遥控器通信信号 跨空间、频域场景 MOANA[88] 导航雷达 雷达接收机 导航雷达信号 跨时间、空间、接收机场景 ATCRBS跨模态数据集[89] 地面雷达 飞机应答机 民航雷达信号 跨空间、时间场景
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