信号处理2025，Vol.41Issue(10)：1693-1702,10.DOI:10.12466/xhcl.2025.10.009
基于AE-VMD和ECAResNet的雷达辐射源个体识别
Radar Specific Emitter Identification Based on AE-VMD and ECAResNet
摘要
Abstract
Radar specific emitter identification is one of the core technologies in electronic support measures and battle-field situational awareness.Existing radar specific emitter identification methods based on Hilbert-Huang Transform(HHT)and deep learning are limited by poor selection of decomposition parameters and low identification accuracy.To address these issues,a radar specific emitter identification method based on variational mode decomposition(VMD)pa-rameter optimization and Efficient Channel Attention-Residual Neural Network(ECAResNet)was proposed,combin-ing intelligent optimization algorithms with signal processing.First,the signal was decomposed into multiple optimal modal components by means of the Alpha evolution(AE)optimization algorithm,combined with VMD to achieve an adaptive optimal decomposition of the parameters;second,the Hilbert transform was applied to the decomposed modal components to construct the Hilbert spectrogram as the network input;finally,the improved ECAResNet was used to extract the global and local features of the Hilbert spectra to achieve efficient recognition.The performance of the pro-posed method was tested using self-acquired USRP datasets,and the experimental results demonstrated that the accuracy of the proposed method was close to 100%in identifying six types of radar emitter individuals at high signal-to-noise ratios(SNRs).Compared with the existing methods based on VMD,the recognition rate of the proposed method was improved by 5.41 and 7.93 percentage points at high SNR,and by 14.58 and 26.88 percentage points at low SNR(0 dB),respectively.This suggests the superior noise immunity performance of the proposed model.Moreover,ablation experiments were de-signed to verify the effect of parameter optimization on the recognition performance.Compared with different recogni-tion networks,the recognition accuracy of ECAResNet at all SNRs was improved by 1 and 6.9 percentage points,re-spectively,under the condition that the network parameters and amount of operation were not significantly different.Thus,the experimental results verified the efficacy of the proposed method in terms of recognition accuracy and noise immunity.关键词
雷达辐射源个体识别/智能优化算法/变分模态分解/注意力机制/希尔伯特谱Key words
radar specific emitter identification/intelligent optimization algorithm/variational modal decomposition/attention mechanism/Hilbert spectrum分类
信息技术与安全科学引用本文复制引用
骆丽萍,余思雨,黄洁,黄东华..基于AE-VMD和ECAResNet的雷达辐射源个体识别[J].信号处理,2025,41(10):1693-1702,10.基金项目
国家自然科学基金(62071490) The National Natural Science Foundation of China(62071490) （62071490）
