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

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 propos...

Full description

Saved in:
Bibliographic Details
Published in:IET radar, sonar & navigation Vol. 12; no. 10; pp. 1082 - 1089
Main Authors: Angelov, Aleksandar, Robertson, Andrew, Murray-Smith, Roderick, Fioranelli, Francesco
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.10.2018
Subjects:
automotive radar transceiver TEF810X
convolutional neural network
deep neural network
Doppler radar
electrical engineering computing
microcontroller unit SR32R274
microcontrollers
microDoppler extraction
NXP Semiconductors
object detection
object tracking
radar detection
radar pre‐processing
radar receivers
radar tracking
radar transmitters
recurrent neural nets
recurrent neural network
residual neural network
road vehicle radar
signal classification
Special Issue: Advanced Automotive Sensing – Towards Car Autonomy
target classification
time 0.5 s
radar transmitters
radar tracking
electrical engineering computing
radar detection
recurrent neural network
recurrent neural nets
convolutional neural network
radar pre-processing
object detection
microcontroller unit SR32R274
NXP Semiconductors
signal classification
target classification
automotive radar transceiver TEF810X
time 0.5 s
residual neural network
radar receivers
object tracking
microDoppler extraction
Doppler radar
road vehicle radar
deep neural network
microcontrollers
ISSN:1751-8784, 1751-8792
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2018.0103