Search Results for author:
Found 8 papers, 0 papers with code
A Survey on Detection, Classification, and Tracking of Aerial Threats using Radar and Communications Systems
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.
A Survey on Detection, Tracking, and Classification of Aerial Threats using Radars and Communications Systems
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.
Comparative Analysis of Radar Cross Section Based UAV Classification Techniques
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.
Hierarchical Learning Framework for UAV Detection and Identification
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.
Semi-supervised Learning Framework for UAV Detection
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.
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies
This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies.
Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning
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.
FPGA prototyping of synchronized chaotic map for UAV secure communication
We propose a design that uses the principle of chaos for UAV secure communication.
