Deep Learning-Based Segmentation for the Extraction of Micro-Doppler Signatures

Conference contribution
(Conference Contribution)


Publication Details

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


Abstract

We present a method for extracting micro-Doppler signatures using a deep
convolutional neural network that learns to identify and separate
relevant micro-Doppler components from the background. A modified
convolutional neural network (fully convolutional network) is trained
end-to-end to perform dense predictions from the micro-Doppler signature
at the input, generating a map with labels on a pixel level at the
output. The network learns intermediate representations with the
characteristic patterns of the micro-Doppler paths generated by
individual scatterers and is capable of identifying and locating them in
the time-frequency representation. The model trained on a simulated
environment shows very good performance metrics even in noisy
environments, and the experimental results with a continuous wave (CW)
radar at 24 GHz indicates that the model can be applied to real
scenarios. Moreover, the method scales properly to more complex
signatures when several components are superimposed in the
time-frequency representation, which indicates that this concept might
represent a promising approach for interpreting complex micro-Doppler
signatures.


FAU Authors / FAU Editors

Martinez Garcia, Javier
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik


How to cite

APA:
Martinez Garcia, J., & Vossiek, M. (2018). Deep Learning-Based Segmentation for the Extraction of Micro-Doppler Signatures. In Proceedings of the Proceedings of the European Microwave Week (EuRAD). Madrid, ES: IEEE.

MLA:
Martinez Garcia, Javier, and Martin Vossiek. "Deep Learning-Based Segmentation for the Extraction of Micro-Doppler Signatures." Proceedings of the Proceedings of the European Microwave Week (EuRAD), Madrid IEEE, 2018.

BibTeX: 

Last updated on 2019-17-04 at 22:23