Database improves drone detection

Aalto University developing drone information database
Measuring a drone's Radar Cross Section in Aalto University's anechoic chamber. (Aalto University)

In the hope of keeping better tabs on drones, researchers from Aalto University (Finland), UCLouvain (Belgium), and New York University (USA) have gathered extensive radar measurement data, aiming to improve the detection and identification of drones. Researchers measured various commercially available and custom-built drone models' Radar Cross Section (RCS), which indicates how the target reflects radio signals. The RCS signature can help to identify the size, shape and the material of the drone.

"We measured drones' RCS at multiple 26-40 GHz millimeter-wave frequencies to better understand how drones can be detected, and to investigate the difference between drone models and materials in terms of scattering radio signals," said the study’s author, researcher D. Sc. Vasilii Semkin. “We believe that our results will be a starting point for a future uniform drone database.”

The measurement data, which will be publicly accessible, can be utilized in developing radar systems, as well as machine learning algorithms for more complex identification. This would increase the probability of detecting drones and reducing fault detections.

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“There is an urgent need to find better ways to monitor drone use. We aim to continue this work and extend the measurement campaign to other frequency bands, as well as for a larger variety of drones and different real-life environments,” said Semkin.

Researchers also suggest that 5G base stations could be used in the future for surveillance.

“We are developing millimeter-wave wireless communication technology, which could also be used in sensing the environment like a radar. With this technology, 5G-base stations could detect drones, among other things,” said professor Ville Viikari from Aalto University.

 

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