Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN

This paper proposes a target classification method using radar frontal imaging measured by millimeter-wave multiple-input multiple-output (MW-MIMO) through deep convolutional neural networks. Autonomous vehicles must classify targets in front of the vehicle to attain better situational awareness. We use 2D sparse array capture images objects on road, such as sedans, vans, trucks, humans, poles, and trees. The image includes information regarding not only shape but also reflection characteristics each part target. are classified networks, rate yielded 87.1% for six classes 92.6% three classes.