信号处理2026，Vol.42Issue(2)：194-207,14.DOI:10.12466/xhcl.2026.02.007
异步采集条件下的通信辐射源个体识别
Communication Emitter Identification Under Asynchronous Acquisition Condition
摘要
Abstract
An asynchronous data acquisition method for communication emitter identification under non-cooperative conditions is proposed to alleviate the problem of recognition drift.Firstly,a measurement signal model is established under asynchronous acquisitions.The impact of time-frequency asynchronous scenarios,including multipath channel in-terference,relative motion of the target,and receiver-specific differences,on the measurement signal is analyzed in de-tail.Time delay and frequency shift are demonstrated to be the primary factors contributing to the divergence of identifi-cation features.Secondly,to mitigate the interference of time delays and frequency shifts on the subtle characteristics of the signal,these effects are transformed into coordinate positions of signal features on time-frequency domain(TFD)us-ing short-time Fourier transform(STFT).A unified time-frequency resolution scale is applied to enhance the stability of signal features.Measurement errors in TFD are derived and analyzed,which highlight that time delay and frequency shift induce position jitter in time-frequency features(TFFs),and thus,indirectly affect feature measurement accuracy.Thirdly,to reduce the influence of TFFs position jitter,refined time-frequency measurement technology is employed to extract fine-grained signal features.An algorithm to eliminate the time-frequency fence effect based on frequency-domain frequency shift compensation is presented.Finally,leveraging the refined TFFs of the signal,emitter individual identification is reformulated as a fine-grained image recognition problem based on deep learning.The effectiveness of the proposed algorithm is validated using the DenseNet201 transfer learning network and image enhancement technique.Field experiments demonstrate that for shipborne automatic identification systems(AIS)of the same type,under asyn-chronous acquisition conditions with varying sampling devices,parameters,time and space,the Top-1 individual identi-fication accuracy rate for 10 communication emitters exceeds 85%.关键词
辐射源个体识别/短时傅里叶变换/异步采集/深度学习Key words
emitter identification/short-Fourier transform/asynchronous acquisition/deep learning分类
信息技术与安全科学引用本文复制引用
黄宇,张鑫,田威,涂建清,陈新..异步采集条件下的通信辐射源个体识别[J].信号处理,2026,42(2):194-207,14.基金项目
国家自然科学基金(61803379) （61803379）
中国博士后科学基金(2017M613370,2018T111129) （2017M613370,2018T111129）
湖北省自然科学基金(2025AFB879) The National Natural Science Foundation of China(61803379) （2025AFB879）
China Postdoctoral Science Foundation(2017M613370,2018T111129) （2017M613370,2018T111129）
Hubei Provincial Natural Science Foundation(2025AFB879) （2025AFB879）
