Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks

Chen, Z., Li, G., Fioranelli, F. and Griffiths, H. (2018) Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 15(5), pp. 669-673. (doi: 10.1109/LGRS.2018.2806940)

Abstract

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node.

• Item Type: Articles
• 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
• Journal Name: IEEE Geoscience and Remote Sensing Letters
• Publisher: IEEE
• ISSN: 1545-598X
• ISSN (Online): 1558-0571
• Published Online: 06 March 2018
• Copyright Holders: Copyright © 2018 8 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
• First Published: First published in IEEE Geoscience and Remote Sensing Letters 15(5): 669-673
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher