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mmTracking: A DL-Based mmWave RADAR Data Processing Algorithm for Indoor People Tracking
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DOI：10.1109/JSEN.2025.3628185.png)
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Abstract
En 中文
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 article presents an original approach to locate and track moving targets in indoor environments, based on a YOLOv3 DL network that can be applied to radar data. To quantify the performance of the proposed method, here named mmTracking, 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 m2, and a root mean square error (RMSE) in the tracking of 0.40 m.
Keywords:
Deep learning (DL)
frequency modulated continuous wave (FMCW) radar
localization
moving people detection
tracking
You Only Look Once (YOLO)
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W