Drone Remote Identification (RID) plays a critical role in low-altitude airspace supervision, yet its broadcast nature and lack of cryptographic protection make it vulnerable to spoofing and replay attacks. In this paper, we propose a consistency verification-based physical-layer authentication (PLA) algorithm for drone RID frames. A RID-aware sensing and decoding module is first developed to extract communication-derived sensing parameters, including angle-of-arrival, Doppler shift, average channel gain, and the number of transmit antennas, together with the identity and motion-related information decoded from previously authenticated RID frames. Rather than fusing all heterogeneous information into a single representation, different types of information are selectively utilized according to their physical relevance and reliability. Specifically, real-time wireless sensing parameter constraints and previously authenticated motion states are incorporated in a yaw-augmented constant-acceleration extended Kalman filter (CA-EKF) to estimate the three-dimensional position and motion states of the drone. To further enhance authentication reliability under highly maneuverable and non-stationary flight scenarios, a data-driven long short-term memory-based motion estimator is employed, and its predictions are adaptively combined with the CA-EKF via an error-aware fusion strategy. Finally, RID frames are authenticated by verifying consistency in the number of transmit antennas, motion estimates, and no-fly-zone constraints. Simulation results demonstrate that the proposed algorithm significantly improves authentication reliability and robustness under realistic wireless impairments and complex drone maneuvers, outperforming existing RF feature-based and motion model-based PLA schemes.

翻译：无人机远程识别在低空空域监管中具有关键作用，但其广播特性与加密保护的缺失使其易受欺骗与重放攻击。本文提出一种基于一致性验证的无人机RID帧物理层认证算法。首先设计RID感知的传感与解码模块，提取通信衍生的传感参数（包括到达角、多普勒频移、平均信道增益与发射天线数量），以及从先前已认证RID帧中解码出的身份与运动相关信息。不同于将所有异构信息融合为单一表征，本研究根据物理相关性与可靠性选择性利用不同类型信息。具体而言，将实时无线传感参数约束与先前已认证运动状态融入偏航增强的恒定加速度扩展卡尔曼滤波器中，以估计无人机的三维位置与运动状态。为在高度机动与非平稳飞行场景下进一步提升认证可靠性，采用数据驱动的长短期记忆运动估计器，并通过误差感知融合策略将其预测与CA-EKF自适应结合。最终，通过验证发射天线数量、运动估计与禁飞区约束的一致性完成RID帧认证。仿真结果表明，所提算法在实际无线损伤与复杂无人机机动条件下显著提升了认证可靠性与鲁棒性，优于现有基于射频特征与运动模型的物理层认证方案。