Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG).
High-resolution three-dimensional (3D) images can be acquired by the planar Multiple-Input Multiple-Output (MIMO) array radar making future work like detection and tracking easier. However, regarding portability and to save the costs of radar system, MIMO radar array adopts sparse type with limited number of antennas, so the imaging performance of a MIMO radar system is limited. In this paper, the 3D back projection imaging algorithm is verified by the experimental results of planar MIMO array for human body and an enhanced radar imaging method is proposed. The Lucy-Richardson (LR) algorithm based on deconvolution that is normally used for optical images is applied in radar images. Since the LR algorithm can amplify the noise level in a noise-contaminated system, a regularization method based on the Total Variation constraint is further incorporated in the LR algorithm to suppress the ill-posed characteristics. The proposed method shows a higher image Signal-to-Noise Ratio, a faster rate of convergence, a higher structure similarity and a smaller relative error compared to some similar methods. In the meantime, it also reduces the loss of image information after image enhancement and improves the radar image quality (get less grating lobe and clearer human limbs). The proposed method overcomes the disadvantages mentioned above and is verified by simulation experiment and real data measurement.
The micro-Doppler effect is a useful signature for classifying various human behaviours. However, most micro-Doppler researches assume that only a single moving target exists during the observation. Their works lack in separating micro-motion features from multi-movers. When more than one target is present, their performance will deteriorate heavily. To address this issue, the authors design a new 3D (threedimensional) model, range-velocity-time points, to separate and describe multi-mover micro-motions measured by the ultra-wideband radar. These 3D points contain the range-velocity-time information simultaneously. By dividing points in the 3D space instead of single Doppler domain, micro-Doppler signatures of each target can be separated effectively. Multi-people motion simulation results verify the effectiveness of the authors' method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.