Accurate Image Separation Method for Two Closely Spaced Pedestrians Using UWB Doppler Imaging Radar and Supervised Learning

Recent studies have focused on developing security systems using micro-Doppler radars to detect human bodies. However, the resolution of these conventional methods is unsuitable for identifying bodies and moreover, most of these conventional methods were designed for a solitary or sufficiently well-spaced targets. This paper proposes a solution to these problems with an image separation method for two closely spaced pedestrian targets. The proposed method first develops an image of the targets using ultra-wide-band (UWB) Doppler imaging radar. Next, the targets in the image are separated using a supervised learning-based separation method trained on a data set extracted using a range profile. We experimentally evaluated the performance of the image separation using some representative supervised separation methods and selected the most appropriate method. Finally, we reject false points caused by target interference based on the separation result. The experiment, assuming two pedestrians with a body separation of 0.44 m, shows that our method accurately separates their images using a UWB Doppler radar with a nominal down-range resolution of 0.3 m. We describe applications using various target positions, establish the performance, and derive optimal settings for our method. key words: human imaging, closely-spaced pedestrians, image separation, UWB Doppler radar, supervised learning, support vector machine (SVM)