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

Content Provider	IET Digital Library
Author	Angelov, Aleksandar Robertson, Andrew Smith, Roderick Murray Fioranelli, Francesco
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.
Starting Page	1082
Ending Page	1089
Page Count	8
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 10, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.0103
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-04-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automotive Radar Transceiver TEF810X Convolutional Neural Network Deep Neural Network Digital Signal Processing Doppler Radar Electrical Engineering Computing Microcontroller Microcontroller Unit SR32R274 MicroDoppler Extraction Microprocessor Chips Microprocessors And Microcomputer Neural Computing Technique NXP SemiConductor Object Detection Object Tracking Radar Detection Radar Equipment Radar Pre-processing Radar Receiver Radar Tracking Radar Transmitters Recurrent Neural Nets Recurrent Neural Network Residual Neural Network Road Vehicle Radar Signal Classification Signal Detection System And Application Target Classification Time 0.5 S
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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