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)

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