数据采集与处理2026，Vol.41Issue(3)：663-673,11.DOI:10.16337/j.1004-9037.2026.03.003
宽度学习驱动的跨时间域无人机个体增量识别
Broad Learning-Driven Cross-Time-Domain Incremental UAV Individual Identifi-cation
摘要
Abstract
Intelligent signal recognition technologies can effectively enhance the performance of individual unmanned aerial vehicle(UAV)identification.However,their practical deployment is significantly constrained by time-varying channel effects and feature distribution drift across domains.With the rapid development of the low-altitude economy,UAV safety supervision urgently demands reliable identity authentication mechanisms.Radio frequency fingerprint(RFF)identification,while inherently difficult to forge owing to hardware uniqueness,suffers from severe feature drift in time-varying channels typical of low-altitude intelligent networks.This drift leads to the catastrophic degradation of generalization in pre-trained models.To address this issue,we propose a broad learning-driven method for cross-time-domain incremental identification of individual UAVs.This method adopts a residual network integrated with multi-scale asymmetric convolutions as the backbone,aiming to extract robust and multi-granularity fingerprint features directly from IQ signals.A broad learning system is subsequently introduced as an incrementally updatable classifier;it rapidly updates model weights for new time-domain data by leveraging the generalized inverse matrix,thereby circumventing catastrophic forgetting.Furthermore,a learnable feature fusion module and an experience replay mechanism are synergistically designed to suppress feature drift across time domains.Extensive experiments are conducted on real-world UAV RF signal datasets collected over multiple time spans,with intervals ranging from days to weeks.The results demonstrate that the proposed method achieves an identification accuracy exceeding 90%on both the source and cross-time domains,outperforming baseline algorithms by over 20%.Meanwhile,it maintains stable recognition performance on data from earlier time periods.The proposed approach effectively mitigates the adverse effects of time-varying domain shift,offering reliable technical support for continuous UAV identity recognition and the detection of unauthorized UAVs in complex environments.关键词
无人机识别/宽度学习/跨时间域/多尺度网络/特征融合Key words
UAV identification/broad learning/cross-time domain/multi-scale network/feature fusion分类
信息技术与安全科学引用本文复制引用
孟玟妤,齐佩汉,刘新阳,潘晨露..宽度学习驱动的跨时间域无人机个体增量识别[J].数据采集与处理,2026,41(3):663-673,11.基金项目
国家自然科学基金(62171334) （62171334）
国家基础科研项目(JCKY2023110C099). National Natural Science Foundation of China(No.62171334) （JCKY2023110C099）
National Basic Scientific Research of China(No.JCKY2023110C099). （No.JCKY2023110C099）
