People | Locations | Statistics |
---|---|---|
Naji, M. |
| |
Motta, Antonella |
| |
Aletan, Dirar |
| |
Mohamed, Tarek |
| |
Ertürk, Emre |
| |
Taccardi, Nicola |
| |
Kononenko, Denys |
| |
Petrov, R. H. | Madrid |
|
Alshaaer, Mazen | Brussels |
|
Bih, L. |
| |
Casati, R. |
| |
Muller, Hermance |
| |
Kočí, Jan | Prague |
|
Šuljagić, Marija |
| |
Kalteremidou, Kalliopi-Artemi | Brussels |
|
Azam, Siraj |
| |
Ospanova, Alyiya |
| |
Blanpain, Bart |
| |
Ali, M. A. |
| |
Popa, V. |
| |
Rančić, M. |
| |
Ollier, Nadège |
| |
Azevedo, Nuno Monteiro |
| |
Landes, Michael |
| |
Rignanese, Gian-Marco |
|
Viikari, Ville
Aalto University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2023Comparison of Additively Manufactured Dual-Polarized Probe Antennas at Ku-Bandcitations
- 2023Dual-Polarized 6–18 GHz Antenna Array with Low-Profile Inverted BoR Elementscitations
- 2020Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequenciescitations
- 2019Integrated Metal-lens Antennas with Reduced Height at 71-76 GHz
- 2018E-Band Beam-Steerable and Scalable Phased Antenna Array for 5G Access Pointcitations
- 2016Realization of RFID tag antenna with 3D printing technologycitations
Places of action
Organizations | Location | People |
---|
article
Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies
Abstract
<p>In this work, we present quasi-monostatic Radar Cross Section measurements of different Unmanned Aerial Vehicles at 26-40 GHz. We study the Radar Cross Section signatures of nine different multi-rotor platforms as well as a single Lithium-ion Polymer battery. These results are useful in the design and testing of radar systems which employ millimeter-wave frequencies for superior drone detection. The data shows how radio waves are scattered by drones of various sizes and what impact the primary construction material has on the received Radar Cross Section signatures. Matching our intuition, the measurements confirm that larger drones made of carbon fiber are easier to detect, whereas drones made from plastic and styrofoam materials are less visible to the radar systems. The measurement results are published as an open database, creating an invaluable reference for engineers working on drone detection.</p>