MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors

Journal article
(Review article)


Publication Details

Author(s): Martinez Garcia J, Zoeke D, Vossiek M
Journal: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2018
Volume: 6
Pages range: 41391-41398
ISSN: 2169-3536
Language: English


Abstract

Radar imaging is a competitive option for smart city applications over
optical approaches, as it raises no privacy concerns. The inherent
difficulty of interpreting radar signals can be overcome using deep
learning techniques to leverage the capabilities of monitoring sensors
with a minimum of human intervention. In this paper, we use a modified
convolutional neural network (CNN) for classifying radar images in order
to detect vacant parking spaces with a 77-GHz imaging radar. Although
training CNNs for radar-image classification is challenging due to poor
generalization performance caused by the lack of labeled training data,
the modified architecture takes into account the properties of the radar
image in order to introduce prior information into the model and
improve performance. A MIMO-FMCW radar is utilized to render a
slant-range image of a parking scenario, and the image patches
corresponding to each parking location are classified independently in
the CNN. Since the radiation pattern of a MIMO array varies as a
function of the scanning angle, the corresponding spatial coordinate of
each patch is included as an additional feature in the upper layers of
the network. This allows the model to combine local features from each
patch with global scenario information in order to learn robust features
that generalize properly to new scenarios. Several models are trained
end to end with data from four different parking scenarios and evaluated
in a 4-fold cross-validation scheme, and performance is improved when
spatial prior information is included.


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., Zoeke, D., & Vossiek, M. (2018). MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors. IEEE Access, 6, 41391-41398. https://dx.doi.org/10.1109/access.2018.2857007

MLA:
Martinez Garcia, Javier, Dominik Zoeke, and Martin Vossiek. "MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors." IEEE Access 6 (2018): 41391-41398.

BibTeX: 

Last updated on 2019-29-01 at 16:23

Share link