Unauthorized unmanned aerial vehicle (UAV) activity around airports, public venues, and other sensitive sites has made protected-airspace monitoring increasingly important. A practical sensing system must search a wide angular region, find small long-range targets, and return both bearing support and UAV-specific evidence before a restricted perimeter is breached. Existing UAV detection paths often rely on spatially organized evidence, such as body extent, silhouette, or track continuity. At long range, however, these cues become difficult to preserve and verify as the target footprint weakens and its image-plane support shrinks. EventRadar follows a complementary cue: propeller-induced temporal periodicity, which recent event-camera sensing studies have shown can reveal UAV-specific motion after appearance becomes weak. We extend this cue to kilometer-scale active sensing with an event-camera prototype. Scene-Anchored Geometry Evidence (SAGE) fuses scanning events with IMU pose to maintain a bearing-indexed scene memory, separating transient candidate support from persistent background clutter. Comb-guided Harmonic-Group Learned Iterative Shrinkage and Thresholding Algorithm (CHG) then treats each candidate as a weak high-rate timing signal and recovers phase-insensitive harmonic evidence with fixed compute. Compared with related event-camera baselines on 700-1500 m UAV event recordings, EventRadar achieves 0.990 mAP$_{.3}$ and 0.949 F1$_{.3}$, reduces FN$_{.3}$ to 0.009, and shows real-time feasibility in prototype profiling.

翻译：未经授权的无人机在机场、公共场所及其他敏感区域周边的活动，使得保护空域监控日益重要。实用的传感系统需要搜索广阔角度区域、发现小型远程目标，并在受限周界被突破前同时提供方位支撑和无人机特异性证据。现有无人机检测路径通常依赖空间组织证据，如机体尺寸、轮廓或航迹连续性。然而，在远程条件下，随着目标足迹减弱及其图像平面支撑缩小，这些线索变得难以维持和验证。EventRadar采用互补线索：螺旋桨引发的时间周期特性。近期事件相机传感研究表明，在目标外观变弱后，该特性可揭示无人机特异性运动。我们将此线索扩展至千米级主动传感，并采用事件相机原型。场景锚定几何证据通过融合扫描事件与惯性测量单元姿态来维护方位索引的场景记忆，将瞬态候选支撑与持久背景杂波分离。随后，梳状引导谐波组学习迭代收缩阈值算法将每个候选视为弱高频定时信号，以固定计算恢复相位不敏感谐波证据。在700-1500米无人机事件记录上与相关事件相机基线相比，EventRadar实现了0.990 mAP$_{.3}$和0.949 F1$_{.3}$，将FN$_{.3}$降至0.009，并通过原型探测证明了其实时可行性。