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The measurement of heartbeat rate and breathing rate for patients with sensitive skin, such as skin with burns, is very difficult to do, especially if the number of patients is large and medical personnel is limited. Therefore, this study seeks to propose a preliminary solution to this problem by proposing a device that can measure the vital signs of several people concurrently, especially the heartbeat rate and breathing rate, without attaching sensors to their skin. This is done using an FMCW (frequency-modulated continuous wave) radar that operates at 77–81 GHz. FMCW radar emits electromagnetic waves towards the chest of several targets and picks up the reflected waves. Then, using signal processing of these reflected waves, each target’s heartbeat rate and breathing rate can be obtained. Our experiment managed to perform concurrent detection for four targets. The experimental results are between 52 and 82 beats per minute for the heartbeat rates and between 10 and 35 breaths per minute for the breathing rates of four targets. These results are in accordance with normal heartbeat rate and normal breathing rate; thus, our research succeeded in proposing a preliminary solution to this problem.

In this study, an interference detection and mitigation method is proposed for frequency-modulated continuous-wave radar systems based on time-domain signal reconstruction. The interference detection method uses the difference in one-dimensional fast Fourier transform (1D-FFT) results between targets and interferences. In the 1D-FFT results, the target appears as a peak at the same frequency point for all chirps within one frame, whereas the interference appears as the absence of target peaks within the first or last few chirps within one frame or as a shift in the target peak position in different chirps. Then, the interference mitigation method reconstructs the interference signal in the time domain by the estimated parameter from the 1D-FFT results, so the interference signal can be removed from the time domain without affecting the target signal. The simulation results show that the proposed interference mitigation algorithm can reduce the amplitude of interference by about 25 dB. The experimental results show that the amplitude of interference is reduced by 20–25 dB, proving the effectiveness of the simulation results.

This paper presents the development, performance, integration, and implementation of a 150 GHz FMCW radar based on a homodyne harmonic mixing scheme for noncontact, nondestructive testing. This system offers high-dynamic-range measurement capabilities up to 100 dB and measurement rates up to 7.62 kHz. Such interesting characteristics make this system attractive for imaging applications or contactless sensing. Numerous samples of different materials and geometries were imaged by taking advantage of the radar’s performance. By taking into account the nonionizing capability of the system, new applicative fields such as food industry and pharmaceutical packaging were explored.

Human identification using gait as a biometric feature has gained significant attention in recent years, showing notable advancements in medical fields and security. A review of recent developments in remote radar-based gait identification is presented in this article, focusing on the methods used, the classifiers employed, trends and gaps in the literature. Particularly, recent trends highlight the increasing use of Artificial Intelligence (AI) to enhance the extraction and classification of features, while key gaps remain in the area of multi-subject detection. In this paper, we provide a comprehensive review of the techniques used to implement such systems over the past 7 years, including a summary of the scientific publications reviewed. Several key factors are compared to determine the most suitable radar for remote gait-based identification, including accuracy, operating frequency, bandwidth, dataset, range, detection, feature extraction, size and number of features extracted, multiple subject detection, radar modules used, AI used and their properties, and the testing environment. Based on the study, it was determined that Frequency-Modulated Continuous-Wave (FMCW) radars were more accurate than Continuous-Wave (CW) radars and Ultra-Wideband (UWB) radars in this field. Despite the fact that FMCW is the most closely related radar to real-world scenarios, it still has some limitations in terms of multi-subject identification and open-set scenarios. In addition, the study indicates that simpler AI techniques, such as Convolutional Neural Network (CNN), are more effective at improving results.

Human activity recognition (HAR) using frequency-modulated continuous wave (FMCW) millimeter-wave radar is a promising alternative to wearable and vision-based systems due to its unobtrusive and privacy-preserving nature. However, modeling multi-dimensional radar data under limited training samples while remaining robust to user and environmental variations is challenging, particularly for edge-based applications. To address this challenge, we propose a lightweight artificial intelligence-based framework for FMCW radar-based HAR that enables accurate and computationally efficient activity recognition on edge devices. The framework processes radar-derived Range-Doppler, Range-Azimuth, and Range-Elevation feature maps as structured multi-dimensional data vectors rather than conventional two-dimensional images, allowing compact representation of motion dynamics and spatial relationships. A lightweight deep learning architecture combining a modified ResNet-18 with depthwise separable convolutions and a bidirectional long short-term memory module is employed to extract spatial–temporal features with reduced complexity. To improve generalization under limited data conditions, we used data augmentation strategies including spatial shifting, intensity scaling with bias shift, horizontal Doppler flipping, and additive Gaussian noise. The framework is evaluated on a newly collected 60 GHz FMCW radar dataset covering seven daily activities in a realistic home-like environment. Experiments using cross-scene and leave-one-person-out validation demonstrate superior performance over baseline methods, achieving up to 91.98% accuracy and 89.82% F1-score.

Hand Gesture Recognition (HGR) using Frequency Modulated Continuous Wave (FMCW) radars is difficult because of the inherent variability and ambiguity caused by individual habits and environmental differences. This paper proposes a deformable dual-stream fusion network based on CNN-TCN (DDF-CT) to solve this problem. First, we extract range, Doppler, and angle information from radar signals with the Fast Fourier Transform to produce range-time (RT) and range-angle (RA) maps. Then, we reduce the noise of the feature map. Subsequently, the RAM sequence (RAMS) is generated by temporally organizing the RAMs, which captures a target’s range and velocity characteristics at each time point while preserving the temporal feature information. To improve the accuracy and consistency of gesture recognition, DDF-CT incorporates deformable convolution and inter-frame attention mechanisms, which enhance the extraction of spatial features and the learning of temporal relationships. The experimental results show that our method achieves an accuracy of 98.61%, and even when tested in a novel environment, it still achieves an accuracy of 97.22%. Due to its robust performance, our method is significantly superior to other existing HGR approaches.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

We introduce a new principle for distance measurement in the terahertz-wave range using a resonant-tunneling-diode (RTD) oscillator as a source at 511 GHz and relying on the frequency-modulated continuous-wave (FMCW) radar technique. Unlike the usual FMCW radar, where the sawtooth frequency modulation is applied to the carrier, we propose applying it to a subcarrier obtained by amplitude modulation; this is advantageous when the source cannot be controlled precisely in oscillation frequency, but can easily be modulated in amplitude, as is the case of the RTD oscillator. The detailed principle and a series of proof-of-concept experimental results are presented.