Novel Hybrid Machine-Learning Technique for Robust Indoor Multi-Subject Tracking Using mmWave Radar

Publication Date

1-28-2026

Document Type

Article

Publication Title

IEEE Internet of Things Journal

DOI

10.1109/JIOT.2026.3658372

Abstract

A novel hybrid machine-learning approach is proposed in this work to carry out robust multi-people tracking using a millimeter-wave (mmWave) radar in complex indoor environments. The proposed new system including the adaptive hybrid clustering technique leverages both density-based spatial clustering (DBSCAN) and expectation-maximization (EM) schemes over the received radar point-clouds to separate individual human trajectories reliably. Meanwhile, the Hungarian algorithm incorporation with the Kalman filter is employed for tracking of individual persons. Furthermore, a condition-number-based outlier detector is designed to filter error-prone radar data to improve the tracking accuracy. Realworld experiments are conducted in various indoor scenarios and the results demonstrate that our proposed new system can achieve an average Euclidean-distance error (EDE) of 38.19 cm in the presence of a single person and that of 44.70 cm in the presence of two persons in our laboratory with the area of 300 cm×300 cm. Our proposed new multi-subject tracking approach also outperforms the existing methods in terms of EDE.

Keywords

adaptive hybrid clustering, condition number, Hungarian algorithm, Indoor multi-subject tracking, Kalman filter, mmWave radar point-cloud, outlier detection

Department

Applied Data Science

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