Name
Title | ||
|---|---|---|
Convolutional neural networks-based aerial target classification using micro-Doppler profiles. |
Abstract | ||
|---|---|---|
In this paper, convolutional neural networks (CNN)-based aerial target recognition is studied by exploiting the targets' micro-Doppler profiles. In order to simulate the targets' scatterings accurately, their realistic computer-aided design (CAD) models are considered. Scattering characteristics of the targets are taken into account for a variety of radar aspects and propeller or blade rotation speeds. Simulation results exhibit that CNN-based schemes would provide raised high speed in aerial target recognition area due to their self-feature learning nature. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | Aerial Target Classification,Micro-Doppler,Convolutional Neural Networks |
Field | DocType | ISSN |
CAD,Kernel (linear algebra),Radar,Computer vision,Pattern recognition,Convolutional neural network,Propeller,Computer science,Scattering,Artificial intelligence,Artificial neural network,Doppler effect | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osman Karabayir | 1 | 1 | 1.39 |
| Mehmet Zahid Kartal | 2 | 0 | 0.34 |
| Okan Mert Yucedag | 3 | 0 | 1.01 |