Monitoring the status of the driver is a crucial aspect of health monitoring inside vehicles as it helps to identify potential health or safety risks that could affect a driver’s ability to operate a vehicle safely. This includes monitoring for fatigue, distraction, or impairment, among other things, which can potentially cause car crashes. Although many solutions for health monitoring in private vehicles have been proposed, the majority of them are inconvenient to use or have the risk of leaking private information. Radars have the potential to address the above drawbacks by their inherent privacy protection and contactless operation in addition to their high accuracy, convenience, affordable price, and resilience to environmental factors. Among many possible radar configurations, millimeter FMCW radars can accurately detect range and monitor displacements that are essential in breathing pattern monitoring. Breathing pattern monitoring is one of the key signatures of the driver’s health. An accurate estimation of the breathing pattern enables the detection of breathing abnormalities, including tachypnea, bradypnea, biot, cheyne–stokes, and apnea. The breathing pattern can be estimated from both the chest and abdomen. For this purpose, we employed two 60 GHz FMCW radars. The proposed algorithm is capable of detecting the mentioned breathing abnormalities through breathing rate (BR) estimation and breath-hold period detection. In addition, the proposed method in this paper estimates BR based on the multiple range bins. We conducted a study on the human radar geometry problem inside a vehicle to determine the accurate number of range bins for BR estimation. The experimental results demonstrate a maximum BR error of 1.9 breaths per minute using the proposed multi-bin technique. In addition, the dual radar fusion system can detect breath-hold periods with minimal false detections.

The antennas and metasurfaces designed for biomedical applications operate primarily in the far-field rather than the near-field. Working in the near-field is considerably more intricate in both design and theory. Furthermore, the proposed methodologies are unsuitable for seamless integration with a radar system that seeks to offer an efficient and streamlined approach to achieve heightened sensing. Our research highlights the potential of metasurfaces in redefining radar-based sensing. By integrating metasurface principles specifically designed for near-field focusing into radar design, we unlock new possibilities for high-resolution biomedical sensing with real-time capabilities.

The detection of targets under the ground is an important procedure that is typically performed by a human non-automatically. Recent studies have automated this process using artificial intelligence (AI) based on radar images. There are three main steps before feeding reconstructed radar images to a neural network. The first step is segmentation which can make the detection task straightforward. We have proposed an Otsu-based segmentation algorithm in this paper. The proposed segmentation algorithm is effectively able to distinguish between all the targets. In the second step before employing AI to detect targets, a local sliding window has been taken into consideration to improve the results. The image is divided into smaller parts by this sliding window after it has been reconstructed. In the third step, two different methods have been considered for data augmentation. The first method is a novel approach for generating synthetic radar data. It is applied before radar image reconstruction based on the summation of two receivers’ signals with different coefficients. In the second augmentation method, some conventional data augmentation methods like flip and rotating are applied to complete this task. To discriminate targets from background, it is necessary to classify input images to AI-based aproaches. This task can be accomplished by classical machine learning approaches like the scalar vector machine (SVM). Gabor filters have been utilized in this paper to extract the features. There also exist two classification approaches using convolutional neural networks (CNN) to automatically detect targets after image reconstruction. Two different CNN have been implemented. Without data augmentation, the SVM-based approach works better than CNN, and its accuracy is 86.9%. Overall, the second CNN algorithm outperformed SVM after the data augmentation by reaching 96% accuracy.

Using micro-doppler signatures is an effective way to classify different types of UAVs, as well as other airborne objects such as birds. To generate signatures for drones, radar measurements are needed; however, these measurements are limited to the types of available drones, the radar parameters, the targets’ range, and the environments in which these measurements are conducted. In this paper, a new method for generating signature datasets is introduced. The method uses full-wave electromagnetic simulation software. Using this method, radar drones’ datasets can be generated using different types, sizes, drone materials, radar parameters, detected range, targets speed, and rotor RPM for rotary drones. A 77 GHz modeled FMCW radar is used to create dataset for classification purposes. Finally, a Convolutional Neural Network (CNN) algorithm is used to classify five types of drones. Based on the results, the classification of the drones is found to exceed 97% accuracy.

Imaging algorithms can make the detection of buried objects easier. These algorithms use reflected power from different scatterers with different time delays. Reflected signals from almost perpendicular angles have a significant weight in the imaging algorithms. One additional main feature of these signals is that they have fewer time delays. In this paper, we investigate two parameters that can enhance the back projection (BP) imaging algorithm. The first parameter is the antenna pattern. It controls the ability of radar to sense different angles. The second one is the delays for a point of image based on different locations of radar. It is noticeable that nearfield imaging algorithms suffer from artifacts more than noise. The ground surface clutter can make artifacts more than other clutters. So, clutter cancellation is an important task. The time gating method accomplishes this task better in ultra-wideband (UWB) radars. An experimental setup is used to demonstrate the presented algorithm improvements.

Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, a drone’s range-doppler signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if the effect of the mechanical control system wasn’t taken into consideration, which may lead to a non-unique signature of a drone while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of a quadcopter and a hexacopter UAVs on their range-doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the two UAVs and its accuracy is found to exceed 90%.

We present a novel radar signal processing technique to identify the presence or absence of a living body in a vehicle using a mm-wave frequency-modulated continuous-wave radar. Unlike traditional detection methods which are mostly based on constant false alarm rate (CFAR), our proposed method extracts and monitors the consistent Doppler effect of received signals from the radar antenna resulting from the consistent breathing of living bodies over time. The proposed method works in all types of cars without the need for threshold definition for tracking as well as no need for training. Hence, the algorithm is more robust, accurate and fast. We assess our proposed signal processing with two phantoms mimicking the breathing of children and with adults in the vehicle in various conditions. The system has been proven to be robust in extensive studies over the course of multiple months.