Practical classification of different moving targets using automotive radar and deep neural networks

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)

[img]
Preview
Text
160241.pdf - Accepted Version

1MB

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
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

University Staff: Request a correction | Enlighten Editors: Update this record