no code implementations • 8 Feb 2024 • Wahab Khawaja, Martins Ezuma, Vasilii Semkin, Fatih Erden, Ozgur Ozdemir, Ismail Guvenc
Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U.
no code implementations • 18 Nov 2022 • Wahab Khawaja, Martins Ezuma, Vasilii Semkin, Fatih Erden, Ozgur Ozdemir, Ismail Guvenc
Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are provided.
no code implementations • 17 Dec 2021 • Martins Ezuma, Chethan Kumar Anjinappa, Vasilii Semkin, Ismail Guvenc
The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms.
no code implementations • 10 Jul 2021 • Olusiji O Medaiyese, Martins Ezuma, Adrian P Lauf, Ayodeji A Adeniran
While several RF devices (i. e., Bluetooth and WiFi devices) operate in the same frequency band as UAVs, the proposed framework utilizes a semi-supervised learning approach for the detection of UAV or UAV's control signals in the presence of other wireless signals such as Bluetooth and WiFi.
no code implementations • 14 Apr 2021 • Olusiji O Medaiyese, Martins Ezuma, Adrian P Lauf, Ismail Guvenc
The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment.
no code implementations • 23 Feb 2021 • Martins Ezuma, Chethan Kumar Anjinappa, Mark Funderburk, Ismail Guvenc
This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies.
no code implementations • 23 Feb 2021 • Olusiji Medaiyese, Martins Ezuma, Adrian P. Lauf, Ismail Guvenc
By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98. 9% at 10 dB SNR.
no code implementations • 24 Dec 2020 • Christian Nwachioma, Martins Ezuma, Olusiji . O. Medaiyese
We propose a design that uses the principle of chaos for UAV secure communication.