An LSTM Approach to Short-range personnel recognition using Radar Signals

Conference paper (2021)

Authors

Zhenghui Li University of Glasgow
Julien Le Kernec University of Glasgow
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Olivier Romain CY Cergy Paris University
Lei Zhang University of Glasgow
Shufan Yang University of Glasgow
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2021 Zhenghui Li, Julien Le Kernec, F. Fioranelli, Olivier Romain, Lei Zhang, Shufan Yang
DOI: https://doi.org/10.1109/RadarConf2147009.2021.9455218
Micro-Doppler signatures LSTM network Phase information Range-time information Radar sensing Personnel Recognition
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

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

Files

09455218_1_.pdf
(.pdf | 1.92 Mb)