A Safe-Distance Control Scheme to Avoid New Infection Like COVID-19 Virus Using Millimeter-Wave Radar
Chen Chen, Pengtao Yue, Jiabao Si, Ning Lv, Jianlong Zhang, Qingqi Pei, Shaohua Wan
Abstract
The continuous mutation of the novel coronavirus (COVID-19) necessitates effective urban public health safety measures. In public spaces, enforcing social distancing is crucial to prevent droplet transmission of the virus. Existing video detection methods are limited by blind spots, especially in densely populated areas. This article introduces an innovative millimeter wave radar method for effective social distancing control. Unlike traditional radar technology, our approach employs energy accumulation and Doppler compensation techniques to reduce preprocessing interference notably and accurately differentiate between human and nonhuman entities. We have fine-tuned the sensor configuration through distance feedback, thereby enhancing detection resolution. Multiple micro-Doppler images at various spatial scales are acquired and concatenated to construct a 3-D data cube. This serves as input to our network model to achieve higher detection accuracy. Our experiments demonstrate that this method surpasses mainstream video and radar detection solutions, offering broader coverage, increased stability, and the ability to discern distances to humans with a resolution as acceptable as 10 cm.