NDLI: Radar-ID: human identification based on radar micro-Doppler signatures using deep convolutional neural networks

Content Provider	IET Digital Library
Author	Cao, Peibei Xia, Weijie Ye, Ming Zhang, Jutong Zhou, Jianjiang
Abstract	Human identification is crucial in various applications, including terrorist attack preventing, criminal seeking, defence and so on. Traditional human identification methods are usually based on vision, biological features, radio-frequency identification cards and so on. In this study, the authors propose an identification method based on radar micro-Doppler signatures using deep convolutional neural networks (DCNNs) for the first time, which can identify human in non-contact, remote and no lighting status. They employ a K-band Doppler radar to acquire the raw signals due to its stationary clutter rejection and movement detection ability as well as its short wavelength which can generate larger Doppler shift. Then short-time Fourier transform is applied to the raw signals to characterise micro-Doppler signatures. They adopt the DCNNs to deal with the spectrograms for human identification problem. The DCNNs can learn the necessary features and classification conditions from raw micro-Doppler spectrograms without employing any explicit features. While the traditional supervised learning techniques relying on the extracted features require domain knowledge of each problem. It is shown that this method can achieve average accuracy ∼97.1% for 4 people, 90.9% for 6 people, 89.1% for 8 people, 85.6% for 10 people, 77.4% for 12 people, 72.6% for 16 people and 68.9% for 20 people.
Starting Page	729
Ending Page	734
Page Count	6
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 7, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2017.0511
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-03-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biological Features Classification Condition Computer Vision And Image Processing Technique DCNNs Deep Convolutional Neural Network Doppler Radar Doppler Shift Feature Extraction Fourier Transforms Human Identification Method Human Identification Problem Image Classification K-band Doppler Radar Larger Doppler Shift Learning in AI MicroDoppler Spectrograms Movement Detection Ability Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Pattern Classification Radar Equipment Radar Imaging Radar MicroDoppler Signatures Radar-ID Radio-frequency Identification Cards Short-time Fourier Transform Stationary Clutter Rejection Statistics System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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