Abstract
This study shows an approach for classifying road users using a 24-GHz millimeter-wave radar. The sensor transmits multiple linear frequency–modulated waves, which enable range estimation and Doppler-shift estimation of targets in the scene. We aimed to develop a solution for localization and classification, which yielded the same performance when the sensor was fixed on ground or mounted on a moving platform such as a car or quadcopter. In this proposed approach, classification was achieved using supervised learning and a set of hand crafted features independent of relative speed between the target and sensor. The proposed model is based on obtaining micro-Doppler information; only one receiver is used. Therefore, in addition to the target reflectivity, no geometrical information is used. For our study, we selected three classes: pedestrians, cyclists, and cars. We then illustrated distinctive micro-Doppler features for each class based on simulations, which we compared with real-world data. Our results confirm that a limited set of low-complexity features yields high accuracy scores when the target’s trajectory does not excessively deviate from the radar’s radial direction.
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