Applied machine learning for power grid security UAV and bird classification based on 5G-A trajectories
The increasing prevalence of low-altitude aerial activities presents significant security challenges to power grid infrastructure, as distinguishing non-cooperative unmanned aerial vehicles from natural birds remains a persistent technical hurdle. Traditional sensing modalities—such as electro-optical/infrared imaging and radar—often falter under complex environmental conditions or suffer from prohibitive deployment costs. Addressing these constraints, this study introduces an applied machine learning framework that classifies these targets by mining kinematic features from 5 G-Advanced (5 G-A) integrated sensing and communication trajectory data. Our approach builds upon a high-fidelity real-world dataset of 5856 trajectories, utilizing a multidimensional feature system—highlighted by a novel clustered heading standard deviation—to capture the fundamental kinematic divergence between electromechanical control and biological flight. Extensive evaluations across lightweight machine learning models reveal that the random forest classifier achieves optimal discriminative performance, yielding an accuracy of 91.68% and an area under the curve of 0.993, significantly outperforming both support vector machine and k-nearest neighbor counterparts. These findings validate 5 G-A trajectory sensing as a robust, low-cost technical paradigm for wide-area low-slow-small target monitoring, establishing a firm foundation for future low-altitude power grid security.