UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features

Ritchie, M; Jones, AM; (2019) Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features. In: Proceedings of the 2019 IEEE Radar Conference (RadarConf). IEEE: Boston, MA, USA. Green open access

[thumbnail of SUBMITTED VERSION.pdf]
Preview
Text
SUBMITTED VERSION.pdf - Accepted Version

Download (938kB) | Preview

Abstract

This paper presents micro-Doppler analysis and classification results from radar measurements of various hand gestures. A new database of 6 individuals completing 4 separate gestures with over 3,000 repetitions was recorded using a 24 GHz Ancortek radar system. The micro-Doppler signatures from these gestures were generated, features extracted and multiple different classifiers applied to this gesture data. A typical micro-Doppler classification process aims to use either a single range bin of data, average over a series of range bins or align all the target signal to a single bin. Different to previous techniques, the paper presents a method that uses multiple ranges bins to produce a spectrogram per range bin in order to represent the observed gesture over all four dimensions of time, Doppler, space and polarization. A comparison of the traditional and the newly proposed technique is shown and the improvements demonstrated are observed to be significant.

Type: Proceedings paper
Title: Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features
Event: 2019 IEEE Radar Conference (RadarConf)
Location: Boston, MA, United States
Dates: 22 - 26 April 2019
ISBN-13: 978-1-7281-1679-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/RADAR.2019.8835782
Publisher version: https://doi.org/10.1109/RADAR.2019.8835782
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Micro-Doppler, Classification, MachineLearning, FMCW Radar, Feature extraction, Spectrogram, Sensors, Radar, Databases, Training, Radio frequency
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10073829
Downloads since deposit
263Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item