Angelov, A., Robertson, A., Murray-Smith, R. and Fioranelli, F. (2018) Practical classification of different moving targets using automotive radar and deep neural networks. IET Radar, Sonar and Navigation, 12(10), pp. 1082-1089. (doi: 10.1049/iet-rsn.2018.0103)
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Abstract
In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Murray-Smith, Professor Roderick and Robertson, Mr Andrew and Fioranelli, Dr Francesco and Angelov, Mr Aleksandar |
Authors: | Angelov, A., Robertson, A., Murray-Smith, R., and Fioranelli, F. |
College/School: | College of Science and Engineering > School of Computing Science College of Science and Engineering > School of Engineering > Systems Power and Energy |
Journal Name: | IET Radar, Sonar and Navigation |
Publisher: | Institution of Engineering and Technology |
ISSN: | 1751-8784 |
ISSN (Online): | 1751-8792 |
Published Online: | 18 May 2018 |
Copyright Holders: | Copyright © 2018 The Institution of Engineering and Technology |
First Published: | First published in IET Radar, Sonar and Navigation 12(10):1082-1089 |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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