Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network

Chen, Z., Li, G., Fioranelli, F. and Griffiths, H. (2019) Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network. In: IEEE Radar Conference, Boston, MA, USA, 22-26 Apr 2019, ISBN 9781728116792 (doi: 10.1109/RADAR.2019.8835796)

Abstract

We propose a novel convolutional neural network (CNN) for dynamic hand gesture classification based on multistatic radar micro-Doppler signatures. The timefrequency spectrograms of micro-Doppler signatures at all the receiver antennas are adopted as the input to CNN, where data fusion of different receivers is carried out at an adjustable position. The optimal fusion position that achieves the highest classification accuracy is determined by a series of experiments. Experimental results on measured data show that 1) the accuracy of classification using multistatic radar is significantly higher than monostatic radar, and that 2) fusion at the middle of CNN achieves the best classification accuracy.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fioranelli, Dr Francesco
• Authors: Chen, Z., Li, G., Fioranelli, F., and Griffiths, H.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2375-5318
• ISBN: 9781728116792
• Copyright Holders: Copyright ©2019 IEEE
• First Published: Published in 2019 IEEE Radar Conference (RadarConf)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy