Convolutional Neural Network Assisted Detection and Localization of UAVs with a Narrowband Multi-site Radar

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Martinez J, Kopyto D, Schütz M, Vossiek M
Publisher: IEEE
Publication year: 2018
Language: English


Abstract

We present an approach to detect and locate non-cooperative UAVs from
their micro-Doppler signature using a narrowband radar in a multi-site
configuration. We describe a method for the localization of rotating
objects with the geometric information obtained exclusively from their
micro-Doppler signatures. This approach only requires very simple
transceivers with CW waveforms, in a cost-effective multi-site
architecture. A convolutional neural network is used to detect and
identify the UAVs by extracting the characteristic features of their
micro-Doppler signature. We present simulated and preliminary
experimental data that show the technical viability of this concept.


FAU Authors / FAU Editors

Kopyto, David
Lehrstuhl für Semantische Audiosignalverarbeitung (AudioLabs)
Schütz, Martin
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik


How to cite

APA:
Martinez, J., Kopyto, D., Schütz, M., & Vossiek, M. (2018). Convolutional Neural Network Assisted Detection and Localization of UAVs with a Narrowband Multi-site Radar. In Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2018). Munich, DE: IEEE.

MLA:
Martinez, Javier, et al. "Convolutional Neural Network Assisted Detection and Localization of UAVs with a Narrowband Multi-site Radar." Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2018), Munich IEEE, 2018.

BibTeX: 

Last updated on 2019-16-04 at 23:23