Vision-Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds

Radar as a remote sensing technology has been used to analyze human activities for decades. Despite all the great features such as motion sensitivity, privacy preservation, penetrability, and more, radar has limited spatial degrees of freedom compared to optical sensors and, thus, makes it challenging to sense crowded environments without prior information. In this article, we develop a dual-sensing system, in which a vision sensor is leveraged to guide digital beamforming in a multiple-input multiple-output radar. Also, we develop a calibration algorithm to align the two types of sensors and show that the calibrated dual system achieves about 2 cm precision in 3-D space within a field of view of
$75^\circ$
×
$65^\circ$
and for a range of 2 m. Finally, we show that the proposed approach is capable of detecting the vital signs simultaneously for a group of closely spaced subjects, sitting and standing, in a cluttered environment, which highlights a promising direction for vital signs detection in realistic environments.