发表机构
Jeonbuk National University（国立全北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机群相对几何防御的刚性隐蔽GNSS欺骗盲点，提出基于绝对锚点的拜占庭鲁棒恢复方法，在仿真及ArduPilot、Gazebo实验中实现高精度位置恢复，明确了相关检测与防御极限。
AI 中文摘要
无人机群协同防御通常会将GNSS位置与测得的无人机间几何结构进行交叉校验。本文表明，这种相对几何信道存在一个结构盲点：常见的缓慢变化平移（刚性隐蔽偏移，RigidShift）会保留所有两两距离，因此对任何仅依赖相对信息的检测器而言都是不可观测的（这是一个规范自由度论证）。我们在距离验证和半定可行性基线方法上验证了这种不可观测性，同时明确将其与机载惯性/GNSS监控器区分开，后者只能发出简单警报，但无法恢复无人机群的真实位置。为量化外部参考何时能恢复可观测性，我们推导了校准锚残差检测器的漂移相关检测下限2γ/(1-t_s/T)，并通过实验确定了额外的检测器特定噪声下限（测得斜率为2.66，预测值为2.67）。随后，我们提出了一种以锚点为核心的集中式恢复流程，该流程可从无人机间测距值重建无人机群几何结构，通过拜占庭鲁棒拟合将其与可信锚点子集对齐，并恢复非锚定无人机的绝对位置。当无干净时段标签时，分段估计器可联合估计锚点漂移、攻击率及攻击起始时间。在统计仿真、ArduPilot软件在环（SITL）实验以及带有渲染视觉锚点的Gazebo实验中，该方法在约10.1米的GNSS漂移下，非锚定无人机位置恢复的中位误差为0.39米（20次随机种子）；在渲染视觉多SITL场景中，该误差为7.1厘米（5次随机种子）。我们还表征了非共线锚点几何结构、锚点覆盖范围、τ→0时的漂移-攻击混叠以及多数锚点被攻陷所施加的明确限制。所有评估均基于仿真，未使用射频欺骗硬件或实体无人机群。
英文摘要
Cooperative UAV-swarm defenses commonly cross-check GNSS positions against measured inter-drone geometry. We show that this relative-geometry channel has a structural blind spot: a common, slowly varying translation (a rigid-covert shift, RigidShift) preserves all pairwise distances and is therefore unobservable to any relative-only detector (a gauge-freedom argument). We validate this blindness on distance-verification and semidefinite-feasibility baselines, while explicitly distinguishing it from onboard inertial/GNSS monitors that can raise a bare alarm but cannot recover the swarm's true position. To quantify when an external reference restores observability, we derive the drift-dependent detection floor $2γ/(1-t_s/T)$ for a calibrated anchor-residual detector and empirically identify an additional detector-specific noise floor (measured slope 2.66 vs. predicted 2.67). We then present a centralized anchor-rooted recovery pipeline that reconstructs swarm geometry from inter-drone ranges, aligns it to a trusted-anchor subset with Byzantine-robust fitting, and recovers the absolute positions of non-anchored drones. A segmented estimator jointly estimates anchor drift, attack rate, and onset when no clean-epoch label is available. Across statistical simulations, ArduPilot software-in-the-loop experiments, and Gazebo experiments with rendered vision anchors, the method recovers the positions of non-anchored drones to a median error of 0.39 m (20 seeds) under approximately 10.1 m of GNSS drift, and to 7.1 cm (5 seeds) in the rendered-vision multi-SITL setting. We also characterize the explicit limits imposed by non-collinear anchor geometry, anchor coverage, $τ\to0$ drift-attack aliasing, and majority anchor compromise. All evaluations are simulation-based and use no RF spoofing hardware or physical swarm.
Comments15 pages, 15 figures, Simulation-based study
