Pedestrian Trajectory based Calibration for Multi-Radar Network

Abstract

In recent years, using radio frequency (RF) signal for pedestrian localization and tracking has aroused great interest of researchers due to its property of privacy protection. With the high spatial resolution, millimeter wave (mmWave) becomes one of the most promising RF technologies in human sensing tasks. Existing mmWave-based localization and tracking approaches can achieve decimeter-level accuracy. However, it’s still extremely challenging to locate and track multiple pedestrians in a complex indoor environment due to target occlusion and multipath effect. To overcome these challenges, it is an opportunity to leverage multiple mmWave radars to form a multi-radar network that monitors pedestrians from different perspectives. In this poster, we address one of the fundamental challenges of building one multi-radar network: How to calibrate the perspectives of different mmWave radars before fusing their data. To reduce the overhead and improve the efficiency, we propose a multi-radar calibration method that determines the position relationship of different radars by tracking the pedestrian trajectory. Our evaluation shows that the proposed method can achieve the average error of (8.7cm, 8.5cm) in 2D position and 0.79° in angle.