# EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing

arXiv: https://arxiv.org/abs/2606.11285 · HTML: https://mlsanity.com/paper/2606.11285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera](https://mlsanity.com/paper/2205.04691/) (2022)
- [Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing](https://mlsanity.com/paper/2511.13100/) (2025)

## Abstract (arXiv)

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.