Human Body Recognition Method Using Diffraction Signal in NLOS Scenario for Millimeter Wave Radar

Millimeter wave (MMW) radar is highly expected to environmentally robust automotive sensor, especially in optically blurred vision. Non-line-of-sight (NLOS) sensing for human detection in automotive radar is another distinct feature of millimeter wave, where a diffraction effect would be exploited t...

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Bibliographic Details
Published inIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium pp. 766 - 769
Main Authors He, Jianghaomiao, Terashima, Shota, Yamada, Hideyuki, Kidera, Shouhei
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.09.2020
Subjects
Automotive radar
Diffraction effect
Feature extraction
Laser radar
Machine learning
Millimeter wave radar
Millimeter-wave (MMW) radar
Non-line-of-sight (NLOS) detection
Nonlinear optics
Pedestrian detection
Radar detection
Supervised machine learning
Support vector machines
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Summary:Millimeter wave (MMW) radar is highly expected to environmentally robust automotive sensor, especially in optically blurred vision. Non-line-of-sight (NLOS) sensing for human detection in automotive radar is another distinct feature of millimeter wave, where a diffraction effect would be exploited to detect an unique signal of human body characterized by respiration or attitude control. In this paper, the machine learning based recognition algorithm is introduced to deal with a diffraction signal of human body in NLOS situation. Several feature extraction schemes are implemented in support vector machine (SVM) recognition to address with lower signal-to-noise ratio (SNR) problem. The experimental data, using MMW radar in NLOS case, show the effectiveness for the use of diffraction signal to discriminate human body from other objects.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9323079