数据采集与处理2026，Vol.41Issue(3)：725-735,11.DOI:10.16337/j.1004-9037.2026.03.008
一种用于频谱数据分类与辐射源识别的多模态融合识别方法
Multimodal Fusion Recognition Method for Spectrum Data Classification and Emit-ter Identification
摘要
Abstract
Electromagnetic(EM)battlefield has become increasingly complex due to the proliferation of heterogeneous communication systems,diverse radar waveforms,and a wide array of data link protocols.Accurate and real-time spectrum situation awareness critically depends on the effective extraction of discriminative features from multi-source,multi-modal EM signals and their fusion into consistent,high-level representations—enabling robust classification and radiation source identification.To address these challenges,this paper proposes a comprehensive recognition framework integrating spectral feature parameter extraction,modulation recognition,protocol identification,and multi-source heterogeneous data fusion.The framework achieves high-fidelity signal characterization under low signal-to-noise ratio(SNR)conditions.First,a hierarchical modulation recognition method is developed based on envelope characteristics,spectral symmetry,and spectral peak count,enabling reliable discrimination among five representative signal types—SSB,FM,FSK,MSK,and AM—as well as TACAN signals.Second,domain-specific communication system features are extracted to construct a data link recognition model with enhanced interpretability and generalization.Third,to handle multidimensional spectral feature fusion,a signal preprocessing pipeline and a dimensionality-reduction fusion model are designed to preserve salient information while reducing redundancy.Furthermore,transfer learning and few-shot learning strategies are integrated to mitigate performance degradation under limited and imbalanced training samples for novel radiation sources.Extensive simulations demonstrate that the proposed framework maintains high recognition accuracy across diverse SNR levels and exhibits strong robustness and generalization capability,effectively overcoming the challenges of low-data regimes and class imbalance.关键词
频谱数据/特征提取/调制识别/辐射源识别/多源数据融合Key words
spectrum data/feature extraction/modulation recognition/radiation source identification/multi-source data fusion分类
信息技术与安全科学引用本文复制引用
王忠思,刘丽燕,杨培消..一种用于频谱数据分类与辐射源识别的多模态融合识别方法[J].数据采集与处理,2026,41(3):725-735,11.基金项目
国防综合研究资助项目(2025B0301009). Comprehensive Study of National Defense(No.2025B0301009). （2025B0301009）
