Needle in a Haystack: Tracking UAVs from Massive Noise in Real-World 5G-A Base Station Data

2026-03-30

AI Summary

This work addresses the challenge of detecting and tracking drones in commercial 5G-Advanced (5G-A) base station point clouds, where drone signals are often overwhelmed by noise exceeding their strength by over two orders of magnitude. To tackle this, the authors propose BSense, the first system to leverage operational 5G-A infrastructure for drone surveillance. BSense introduces a three-tier collaborative denoising framework operating at the point, object, and trajectory levels: it employs signal fingerprint modeling for point-level noise suppression, applies multi-frame spatial–velocity consistency checks for object-level filtering, and utilizes a Transformer-based model to enhance trajectory robustness. Evaluated in a real urban environment, BSense reduces the average false detections per frame from 168.05 to 0.04, achieves an F1 score of 95.56%, and attains a mean localization error of only 4.9 meters within a 1000-meter range.

Abstract

The potential usage of UAVs in daily life has made monitoring them essential. However, existing systems for monitoring UAVs typically rely on cameras, LiDARs, or radars, whose limited sensing range or high deployment cost hinder large-scale adoption. In response, we develop BSense, the first system that tracks UAVs by leveraging point clouds from commercial 5G-A base stations. The key challenge lies in the dominant number of noise points that closely resemble true UAV points, resulting in a noise-to-UAV ratio over 100:1. Therefore, identifying UAVs from the raw point clouds is like finding a needle in a haystack. To overcome this, we propose a layered framework that filters noise at the point, object, and trajectory levels. At the raw point level, we observe that noise points from different spatial regions exhibit distinguishable and consistent signal fingerprints, which we can model to identify and remove them. At the object level, we design spatial and velocity consistency checks to identify false objects, and further compute confidence scores by aggregating these checks over multiple frames for more reliable discrimination. At the final trajectory level, we propose a Transformer-based network that captures multi-frame motion patterns to filter the few remaining false trajectories. We evaluated BSense on a commercial 5G-A base station deployed in an urban environment. The UAV was instructed to fly along 25 distinct trajectories across 54 cases over 7 days, yielding 155 minutes of data with more than 14,000 frames. On this dataset, our system reduces the number of false detections from an average of 168.05 per frame to 0.04, achieving an average F1 score of 95.56% and a mean localization error of 4.9 m at ranges up to 1,000 m.

Problem

• UAV tracking
• 5G-A base station
• point cloud noise
• needle-in-haystack detection
• massive interference

Innovation

• 5G-A base station
• UAV tracking
• point cloud denoising
• multi-level filtering
• Transformer-based trajectory validation