Mar 19 2020
Unmanned aerial vehicles (UAV), or drones, are utilized extensively in shipping, aerial photography, mapping, agriculture, rescue operations, law enforcement, among other things.
Measuring a drone’s Radar Cross Section in Aalto University’s anechoic chamber. Image Credit: Vasilii Semkin.
Although there is a great possibility of enhancing public safety, the usage of drones can also result in highly undesirable circumstances, such as property damage or safety and privacy violations. Moreover, there is a highly concerning issue of the use of drones for terrorist attacks, indicating a risk to national security and public safety.
One of the solutions to detect the presence of drones and avoid probable risks is to use radar technology. Since drones come in different shapes, sizes, and composite materials, they could be hard to detect.
Scientists from Aalto University (Finland), New York University (USA), and UCLouvain (Belgium) have collected comprehensive radar measurement data, with the intention to enhance the identification and detection of drones.
The researchers measured the Radar Cross Section (RCS) of several custom-built and commercially available drone models. This implied the way target reflected radio signals. Therefore, the RCS signature can assist to find out the shape, size, and material of the drone.
We measured drones’ RCS at multiple 26-40 GHz millimetre-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. We believe that our results will be a starting point for a future uniform drone database. Therefore, all results are publicly available along with our research paper.
Vasilii Semkin, D.Sc. (Tech), Study Author and Research Scientist, VTT Technical Research Centre of Finland
The measurement data is openly available and can be used in machine learning algorithms for highly complex identification and for developing radar systems. Thus, the possibility of detecting drones and decreasing fault detections will be improved.
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.
Vasilii Semkin, D.Sc. (Tech), Study Author and Research Scientist, VTT Technical Research Centre of Finland
The scientists propose the construction of 5G base stations in the future for surveillance.
We are developing millimetre-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.
Ville Viikari, Professor, School of Electrical Engineering, Aalto University
The outcomes of this study have been reported in IEEE Access, in a special section on Millimetre-wave and Terahertz Propagation Channel Modelling and Applications.
Detecting different drone based on their radar cross section. Open source database.
In the video, the authors explain why we need to detect drones and how the radar technology can be utilized. Video Credit: Aalto University.
Source: https://www.aalto.fi/en