Radar for malicious drones


Anyone can use a drone. Their use is increasingly varied, to the point of endangering our privacy and security. UCLouvain and VTT Aalto in Finland researchers, studied several drones from all angles to learn how to detect them more effectively.

Its sound and slow flight evoke a bumblebee. You've probably come across one at a festival or on vacation, piloted with great care by a photography, film or technology fan. Drones – small remote-controlled aircraft – are now available to the general public. And we find them everywhere. Previously confined to the military, today they fly over our cities and countryside, not without danger. Mr Pairon, a UCLouvain FRIA PhD fellow, conducted several experiments on these small technological marvels at the UCLouvain Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM). The results have just been published in the scientific journal IEEE Access.

From leisure to police security, the drone is everywhere

Today, the drone is used for a wide variety of activities. With a drone, you can take photos or shoot movies. In precision farming, it can analyse certain crops in order to care for each plant. In archaeology, aerial views of difficult-to-access areas are invaluable to advancing research. Police use drones to monitor crowds.

And tomorrow?

For the future, we can imagine almost anything we want with drones’, Mr Pairon explains. ‘The only limit is the batteries, which are heavy.’ Amazon recently promised to use drones to deliver packages in less than 30 minutes. On the telecommunications side, drones may in the future facilitate communication between people or objects in situations where the mobile phone network is no longer operational. ‘In the event of a natural disaster, for example, drones could offer a communication service. During an important event, to prevent the network from being saturated, drones could provide additional capacity over a specific area.

Radar for detecting malicious drones

Alongside these futuristic and idealistic uses, drones can also create danger. They can pass over your home, violating your privacy; over airports, nuclear power plants or military zones. This pushed Mr Pairon to ask: How can we detect the presence of drones reliably and automatically? To answer this question, the research team used radar – the same technology installed along roads. A radar can detect a moving object, using the Doppler effect. The radar sends an electromagnetic wave with a certain energy; the object returns some of the energy to the radar, indicating its speed and location. But how do you know it’s a drone and not a plane or a bird? UCLouvain studied the radar response of several drones in great detail in order to establish a database of categories.

Nine drones from every angle

Experiments consisted of taking measurements of nine different drones from all possible angles. To do this, each drone was subjected to electromagnetic waves in an anechoic (or ‘deaf’) chamber, which absorbs sound and electromagnetic waves. Thanks to this absorption of other electromagnetic waves, only the response of the drone could be measured. ‘These measurements were carried out by Vasilii Semkin, a postdoctoral fellow at Finland’s Aalto University, who visited UCLouvain,’ Mr Pairon says. ‘So I collaborated in analysing the data when he came to Belgium. However, UCLouvain also has an anechoic chamber, if other measurements have to be carried out.

Open source database

Thomas Pairon has built a substantial database (nine models have been analysed at 15 frequencies – between 26 and 40 GHz – and observed at 30,000 different angles for two polarisations, or 10 million points in all). It has just been published in the scientific journal IEEE Access. ‘The database is available for free in open source and will be used by other research teams who can, for example, work on classifying these drones. Other universities can also flesh out the work with their own data.’ What are the next steps for this research? ‘So far, we have measured the radar cross-section (RCS), the shape and backscattering capacity of the electromagnetic signal. What would be interesting to analyse is the micro-Doppler effect, that is, the different parts of the drone in motion, such as the rotation of the propellers. This would detect not only the presence of drones but also their type.’ In the longer term, Mr Pairon imagines being able to use a computer to perform simulations of radar responses. ‘We could model the drone itself on a computer, analyse its response to waves, then simulate for other drones and thus generalise the measures taken.’ These analyses will be an opportunity for UCLouvain research teams to collaborate.

Lauranne Garitte

V. Semkin et al., "Analyzing Radar Cross Section signatures of diverse drone models at mmWave frequencies," in IEEE Access. doi: 10.1109/ACCESS.2020.2979339 lien

A glance at Thomas Pairon's bio

Thomas Pairon earned a master's degree in electrical engineering from UCLouvain in 2015. In 2016, he obtained a FRIA scholarship from the FNRS to finance his thesis work co-supervised by Profs Christophe Craeye and Claude Oestges (UCLouvain/ICTEAM). His work focuses on numerical diffraction modelling by large curved objects via asymptotic methods; developing numerical methods for analysing large antenna networks, including mutual coupling; and impact modelling of the human body on millimetre wave communications.

Published on March 13, 2020