A LSTM Approach to Short-Range Personnel Recognition Using Radar Signals

Li, Z., Le Kernec, J. , Fioranelli, F. , Romain, O., Zhang, L. and Yang, S. (2021) A LSTM Approach to Short-Range Personnel Recognition Using Radar Signals. In: 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA, 7-14 May 2021, ISBN 9781728176093 (doi: 10.1109/RadarConf2147009.2021.9455218)

Abstract

In personnel recognition based on radar, significant research exists on statistical features extracted from the micro-Doppler signatures, whereas research considering other domains and information such as phase is less developed. This paper presents the use of deep learning methods to integrate both phase and magnitude features from range profiles and spectrogram. The temporal features of both domains are separately extracted using a stack of Long Short Term Memory (LSTM) layers. Then, the extracted features are aggregated in the corresponding domains and pass through a series of dense layers with SoftMax classifier. Finally, the information from the two domains is fused with a soft fusion approach to improve the performance further. Preliminary results show that the proposed network with soft fusion can achieve 85.5% accuracy in personnel recognition with six subjects.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Romain, Professor Olivier and Yang, Dr Shufan and Zhang, Professor Lei and Fioranelli, Dr Francesco and Le Kernec, Dr Julien and Li, Zhenghui
• Authors: Li, Z., Le Kernec, J., Fioranelli, F., Romain, O., Zhang, L., and Yang, S.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2375-5318
• ISBN: 9781728176093
• Published Online: 18 June 2021
• Copyright Holders: Copyright © IEEE 2021
• First Published: First published in 2021 IEEE Radar Conference (RadarConf21)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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