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Millimeter wave (mmWave) radar sensors are emerging as valid alternatives tocameras for the pervasive contactless monitoring of people in indoor spaces.However, commercial mmWave radars feature a limited range (up to $6$-$8$ m) andare subject to occlusion, which may constitute a significant drawback in large,crowded rooms characterized by a challenging multipath environment. Thus,covering large indoor spaces requires multiple radars with known relativeposition and orientation and algorithms to combine their outputs. In this work,we present ORACLE, an autonomous system that (i) integrates automatic relativeposition and orientation estimation from multiple radar devices by exploitingthe trajectories of people moving freely in the radars' common fields of view,and (ii) fuses the tracking information from multiple radars to obtain aunified tracking among all sensors. Our implementation and experimentalevaluation of ORACLE results in median errors of $0.12$ m and $0.03^\circ$ forradars location and orientation estimates, respectively. Fused trackingimproves the mean target tracking accuracy by $27\%$, and the mean trackingerror is $23$ cm in the most challenging case of $3$ moving targets. Finally,ORACLE does not show significant performance reduction when the fusion rate isreduced to up to 1/5 of the frame rate of the single radar sensors, thus beingamenable to a lightweight implementation on a resource-constrained fusioncenter.

ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking