Open AccessHuman identification based on radar micro‐Doppler signatures separation
Author(s) -
Qiao Xingshuai,
Shan Tao,
Tao Ran
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3380
Subject(s) - spectrogram , torso , radar , artificial intelligence , signal (programming language) , computer science , doppler radar , convolutional neural network , pattern recognition (psychology) , identification (biology) , doppler effect , computer vision , separation (statistics) , speech recognition , telecommunications , physics , machine learning , medicine , anatomy , programming language , botany , astronomy , biology
In this Letter, the authors propose a method for personnel recognition using deep convolutional neural networks (DCNNs) based on human micro‐Doppler (m‐D) signal separation. In which, the m‐D separation algorithm is firstly performed to separate m‐D signal induced by limbs movement and Doppler signal caused by torso motion, which can highlight the difference contained limbs’ m‐D signatures between the same activity of different people. Afterwards, a five‐layer DCNN is used to learn the necessary features directly from the separated m‐D spectrogram of walking human and then implement human identification task. The method is validated on real data measured with a 5.8 GHz radar system. Experimental results show that an average recognition accuracy of about 90% can be achieved for different human group sizes.