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

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 38.19cm in the presence of a single person and that of 44.70cm in the presence of two persons in our laboratory with the area of 300cm×300cm. Our proposed new multi-subject tracking approach also outperforms the existing methods in terms of EDE.