Abstract
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.
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