Work

Localisation of Drone Controllers from RF Signals using a Deep Learning Approach

Year: 2018

Type: conference-paper

Abstract: Despite their many uses, small commercial Unmanned Aerial Systems (UASs) or drones pose significant security risks. There is, therefore, a need to find methods of detecting, localising and countering these vehicles. This paper presents work towards autonomously localising drone controllers from the Radio Frequency (RF) signals they emit. An RF sensor array is used to monitor the signal spectrum. A Convolutional Neural Network (CNN) is trained to be able to predict the bearing of the drone controller, relative to the sensor, given its output. The position of the controllers can then be calculated from these bearings, provided that at least two such sensors are deployed a reasonable distance apart. The model is able to achieve a mean absolute error of 3.67° in bearing calculation, which translates into a moderate positional error of 40m at a range of 500m.

Source: International Conference on Pattern Recognition and Artificial Intelligence (PRAI)

DOI: 10.1145/3243250.3243272

Authors: David Shorten, Ashley Williamson, Saket Srivastava, John C. Murray

Institutions: University of Hull, University of Lincoln

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Innovate UK

Award: Autonomous and Intelligent UAV Detection