No abstract

Heartbeat detection could enable various applications in the medical and health care fields. In particular, non-contact heartbeat detection can be acceptable for those who have difficulty wearing devices, such as burn patients and infants. A Doppler sensor could be a key device to realize heartbeat detection without any wearable devices. Many researches in recent years have focused on heartbeat detection using a Doppler sensor with a single beam Doppler sensor. However, when the SNR (Signal-to-Noise Ratio) of heartbeat components is low for the beam direction, the heartbeat detection accuracy is likely to degrade. In this paper, for more accurate heartbeat detection, we propose a heartbeat detection method based on beam diversity using a multibeam Doppler sensor. Through the preliminary experiments, we clarified that the SNRs of heartbeat components differ from one beam to another. This means that when the SNR is low for one beam, the SNR could be high for the other beams. Inspired by this fact, the proposed method extracts heartbeat components from all peaks detected by the multi-beam signals. To verify the heartbeat detection accuracy of our method, we conducted the experiments for different detection ranges. The obtained results show that compared to the conventional methods using a single beam, our proposed method using multibeam detected heartbeat more accurately. This indicates the benefit of exploiting beam diversity, i.e., the heartbeat detection accuracy can be improved by utilizing beam diversity.

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person's position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.