Dataset-8: Experimental Study of Outdoor UAV Localization and Tracking Using Passive RF Sensing

2023

2.4 GHz, 2021, Drone, Localization, Rural, Trajectory

Keysight RF Sensor, Non-Canonical Experiments

Passive RF Sensing, UAV Localization

Lead Experimenter

Udita Bhattacherjee, NC State University

Link to Dataset

Link to Dataset:
[IEEE Dataport]

Publications Using This Dataset

A. H. F. Raouf, D. Lee, M. Rahman, S. Masrur, G. Reddy, C. Dickerson, M. S. Hossen, S. Vargas Villar, A. Gürses, S. Singh, S. J. Maeng, M. Ezuma, C. Roberts, M. R. Sarbudeen, T. J. Zajkowski, M. Mushi, O. Ozdemir, R. Asokan, I. Guvenc, M. L. Sichitiu, R. Dutta, “Curated Wireless Datasets for Aerial Networks Research,” Nature npj Wireless Technology, June 2026.
U. Bhattacherjee, E. Ozturk, O. Ozdemir, I. Guvenc, M. L. Sichitiu, H. Dai, “Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing,” Proc. ACM WiNTECH Workshop, February 2022.

Equipment and Software Used

Keysight RF Sensors, Keysight Geolocation Software, and commercial UAVs (this is a BYOD experiment)

Description

Commercial UAVs are flown with a random pattern. Four RF sensors capture the wireless signal passively from the drone, and the RF sensor network tracks the location of the drone using TDOA-based localization.

Representative Results

Result figures
The images below show the experiment area in Dorothea Dix Park with 4 RF sensors and representative results for tracking the location of the drone.

Potential use cases for this dataset include:

Developing UAV Motion Models for UAV Tracking
Enhanced UAV Traffic Management
Search and Rescue Operations
Security and Surveillance