mmTracking: A DL-Based mmWave RADAR Data Processing Algorithm for Indoor People Tracking

Locating and tracking targets in indoor environments is a challenging field of research. The complexity and variability of the environment limit the suitability of many technologies for this application. In this context, mmWave frequency modulated continuous wave (FMCW) radars can prove to be valuable sensors when combined with deep learning (DL) techniques, in order to extend performance in target locating and tracking. This paper presents an original approach to locate and track moving targets in indoor environments, based on a YOLOv3 deep learning network that can be applied to radar data. To quantify the performance of the proposed method, here named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mmTracking</i>, tests were designed in accordance with the ISO/IEC 18305:2016 reference standard. The results show a mean error in localization of 0.39 m with a variance of 0.01 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and a root mean square error (RMSE) in the tracking of 0.40 m.