Person identification using micro-doppler signatures of human motions and UWB radar

Yang, Yang, Hou, Chunping, Lang, Yue, Yue, Guanghui, He, Yuan, and Xiang, Wei (2019) Person identification using micro-doppler signatures of human motions and UWB radar. IEEE Microwave and Wireless Components Letters, 29 (5). pp. 366-368.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


As a typical task of passive biometrics, behavior-based person identification has been studied extensively in recent years. This letter proposes the use of the ultrawideband impulse radar for person identification based upon the micro-Doppler signatures of human motions. A new convolutional neural network (CNN) architecture is proposed for taking advantage of the hierarchical features. The experimental results show that, by utilizing the micro-Doppler signatures of the six selected human motions, the task of person identification can be accurately achieved. Both traditional algorithms and landmark CNN algorithms are chosen for comparison, and the proposed model performs better than the others. Especially when the motion of "running" is adopted to identify persons, the model achieves 95.21% accuracy on the identification of 15 people.

Item ID: 58432
Item Type: Article (Research - C1)
ISSN: 1558-1764
Keywords: convolutional neural network (CNN), micro-Doppler, person identification, ultrawideband (UWB) impulse radar
Copyright Information: © 2019 IEEE.
Date Deposited: 29 May 2019 07:50
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100%
Downloads: Total: 2
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page