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

TL;DR

ORACLE is an autonomous mmWave radar system that accurately estimates relative positions and fuses data from multiple radars for robust indoor people tracking, even under occlusion and resource constraints.

Contribution

It introduces a self-calibrating, multi-radar fusion system that improves tracking accuracy and robustness in indoor environments with occlusion.

Findings

Median errors of 0.12 m and 0.03° in position and orientation estimation.

27% improvement in tracking accuracy through data fusion.

Maintains performance with reduced fusion rates up to 1/5 of sensor frame rate.

Abstract

Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to cameras for the pervasive contactless monitoring of people in indoor spaces. However, commercial mmWave radars feature a limited range (up to $6$-$8$ m) and are 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 relative position and orientation and algorithms to combine their outputs. In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors. Our implementation and experimental evaluation of ORACLE results in median errors of $0.12$ m and $0.03^\circ$ for radars location and orientation estimates, respectively. Fused tracking improves the mean target tracking accuracy by $27\%$, and the mean tracking error is $23$ cm in the most challenging case of $3$ moving targets. Finally, ORACLE does not show significant performance reduction when the fusion rate is reduced to up to 1/5 of the frame rate of the single radar sensors, thus being amenable to a lightweight implementation on a resource-constrained fusion center.