This paper proposes to remotely estimate a human subject’s blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject’s chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP.

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

This paper proposes a target classification method using radar frontal imaging measured by millimeter-wave multiple-input multiple-output (MW-MIMO) radar through deep convolutional neural networks. Autonomous vehicles must classify targets in front of the vehicle to attain better situational awareness. We use 2D sparse array radar to capture the frontal images of objects on the road, such as sedans, vans, trucks, humans, poles, and trees. The frontal image includes information regarding not only the shape of a target but also the reflection characteristics of each part of the target. The measured frontal images are classified by deep convolutional neural networks, and the classification rate yielded 87.1% for six classes and 92.6% for three classes.

This paper investigates the feasibility of extracting the characteristics of sealed paper boxes based on range profiles using a millimeter-wave automotive FMCW radar. Radar is one of the key basic sensors for driving assistance and collision avoidance. Target classification using radar has been studied extensively, yet the detection of sealed material requires further investigation to improve situational awareness for better judgment. We suggest the classification of sealed paper boxes with different materials based on range-profile plots, which capture wave reflection from the box and wave attenuation when traveling through a lossy material. We measure the range profiles of sealed boxes encompassing paper in five different quantities using a millimeter-wave FMCW radar. A theoretical approach is used as a proof of concept, which supports the results of the range-profile measurements. The range profiles of the box with several other materials are also obtained and their characteristics are compared.