宽度学习驱动的跨时间域无人机个体增量识别OA

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.