Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing

Citation Author(s):
North Carolina State University
North Carolina State University
Mihail L.
North Carolina State University
Submitted by:
Udita Bhattacherjee
Last updated:
Thu, 05/12/2022 - 00:27
Link to Paper:
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Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and
communities. In this context, detection and localization of UAVs will be critical for maintaining safe and secure airspace in the
future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring
the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various
other UAVs’ RF signatures in the Keysight database using an envelope detection algorithm. Afterward, time difference of arrival
(TDoA) based localization is performed by a central controller using the sensor data, and the drone is localized with some error.
To mitigate the localization error, implementation of an extended Kalman filter (EKF) is proposed in this study. The performance
of the proposed approach is evaluated on a realistic experimental dataset. EKF requires basic assumptions on the type of motion
throughout the trajectory, i.e., the movement of the object is assumed to fit some motion model (MM) such as constant velocity
(CV), constant acceleration (CA), and constant turn (CT). In the experiments, an arbitrary trajectory is followed, therefore it is
not feasible to fit the whole trajectory into a single MM. Consequently, the trajectory is segmented into sub-parts and a different
MM is assumed in each segment while building the EKF model. Simulation results demonstrate an improvement in error statistics
when EKF is used if the MM assumption aligns with the real motion.


"Inspiron_backup.csv" is the field data collected by the Keysight RF sensor and "GPS_Flight1_backup.csv" is the data collected by a separate GPS app (not from the UAV GPS).

These two datasets was used in the paper "Experimental study of outdoor UAV localization and tracking using passive RF sensing" (https://dl.acm.org/doi/abs/10.1145/3477086.3480832). Please cite our paper if you use these datasets.

Funding Agency: 
NSF PAWR program, INL Laboratory Directed Research Development (LDRD) Program