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In driver assistance or self-driving systems, millimeter-wave radar is an indispensable sensing tool because of its applicability to all weather conditions or non-line-of-sight (NLOS) sensing. This study focuses on a human recognition issue in the NLOS scenario by applying the support vector machine (SVM)-based machine learning approach to a diffraction signal.We show that there is a significant difference in diffraction signals between man-made objects (e.g., metallic cylinder and human body) even without motion.Hence, by exploiting such difference, an SVM achieves a high recognition rate, even in deeply NLOS situations. The experimental investigation, using a 24-GHz millimeter-wave radar in an anechoic chamber demonstrates that a diffraction signal-based recognition accurately classifies the real human and human mimicking man-made object, even in the NLOS scenario shielded by the parking vehicle.

Diffraction Signal-Based Human Recognition in Non-Line-of-Sight (NLOS) Situation for Millimeter Wave Radar