Search Results (417)

Radar-based gesture recognition technology has gained increasing attention in the context of contactless human–computer interaction (HCI). Micro gestures have smaller motion amplitudes and shorter duration compared with traditional gestures, which increases the difficulty of motion feature extraction. In addition, improving recognition accuracy while maintaining low computational and storage costs is also a challenge. In this paper, a micro gesture recognition method combining multi-dimensional feature fusion and a lightweight CQ-MobileNetV3 network is proposed. For feature extraction, the range–time map, velocity–time map, and angle–time map of gestures are first constructed. Then, normalization and adaptive filtering are performed to refine the three maps. Finally, the three refined maps are fused to form a range–velocity–angle–time map, which can accurately describe the motion characteristics of gestures. For recognition, a lightweight CQ-MobileNetV3 network is designed. First, the network structure of MobileNetV3 is optimized to reduce computational complexity. Then, the improved convolutional block attention module (CBAM) and the improved self-attention (SA) module are constructed and integrated into different bottleneck blocks to improve recognition accuracy. A series of experiments are conducted with a 77 GHz frequency-modulated continuous wave (FMCW) radar. The results indicate that CQ-MobileNetV3 achieves a recognition accuracy of 97.16% for 14 micro gestures, with a parameter count of 0.207 M and a computational complexity of 0.027 GFLOPs, surpassing several other deep neural networks. Full article

This paper presents a terahertz (THz) dual-band transmitter for a wideband sparse synthetic bandwidth radar. The transmitter employs an innovative single-path-reuse dual-band architecture. This architecture utilizes a proposed quad-transformer-coupled voltage-controlled oscillator (VCO) as an on-chip local oscillator source. It also incorporates an innovative dual-harmonic generator and a dual-band antenna, which work together within the single signal path to generate both the fundamental frequency and its second harmonic, thereby creating the dual bands required for a sparse synthetic bandwidth radar. Fabricated in a TSMC 40 nm CMOS technology, measurement results show that the transmitter achieves a peak equivalent isotropically radiated power (EIRP) of −7.95 dBm in the low-frequency band (121.34∼126.85 GHz) and −7.86 dBm in the high-frequency band (242.68∼253.7 GHz), validating the proposed architecture’s capability to generate dual-band signals simultaneously. The entire chip occupies a compact area of only 0.54 × 0.62 ${\mathrm{m}\mathrm{m}}^2$ and consumes 136 mW of DC power. Full article

Detecting human movement through walls is vital for disaster response and security, where obstacles obscure visibility and endanger rescue operations, necessitating advanced through-wall radar (TWR) solutions. This study introduces a novel method to enhance velocity estimation accuracy in TWR systems despite signal attenuation caused by walls. Our proposed method dynamically adjusts the beamforming range based on radar-target distance, improving point cloud reconstruction and enabling precise velocity measurements. We conducted experiments in 1D and 2D indoor environments, with and without a brick wall, to validate the proposed method’s effectiveness. In both 1D and 2D experiments, the proposed method successfully restored the velocity information of five subjects located behind a brick wall, achieving root mean square error (RMSE) values of approximately 0.1–0.2 m/s in most cases. Furthermore, statistical comparisons before and after applying the proposed method in the brick wall environment revealed significant reductions in RMSE (p < 0.05) and significant increases in the number of detected point clouds (p < 0.05), confirming the method’s effectiveness in enhancing both velocity extraction accuracy and detection capability. Full article

Ship berthing operations are inherently challenging for maritime vessels, particularly within restricted port areas and under unfavorable weather conditions. Contrary to autonomous open-sea navigation, autonomous ship berthing remains a significant technological challenge for the maritime industry. Lidar and optical camera systems have been deployed as auxiliary tools to support informed berthing decisions; however, these sensing modalities are severely affected by weather and light conditions, respectively, while cameras in particular are inherently incapable of providing direct range measurements. In this paper, we introduce a comprehensive, Radar-Based Berthing-Aid Dataset (R-BAD), aimed to cultivate the development of safe berthing systems onboard ships. The proposed R-BAD dataset includes a large collection of Frequency-Modulated Continuous Wave (FMCW) radar data in point cloud format alongside timestamped and synced video footage. There are more than 69 h of recorded ship operations, and the dataset is freely accessible to the interested reader(s). We also propose an onboard support system for radar-aided vessel docking, which enables obstacle detection, clustering, tracking and classification during ferry berthing maneuvers. The proposed dataset covers all docking/undocking scenarios (arrivals, departures, port idle, and cruising operations) and was used to train various machine/deep learning models of substantial performance, showcasing its validity for further autonomous navigation systems development. The berthing-aid system is tested in real-world conditions onboard an operational Ro-Ro/Passenger Ship and demonstrated superior, weather-resilient, repeatable and robust performance in detection, tracking and classification tasks, demonstrating its technology readiness for integration into future autonomous berthing-aid systems. Full article

Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of $0.974\pm 0.016$ and an accuracy of $99.05\pm 0.55$ %, further supported by high precision of $98.77\pm 1.05$ %, recall of $96.07\pm 2.16$ %, and specificity of $99.73\pm 0.23$ %. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article

This paper presents a bistatic 60 GHz frequency-modulated continuous-wave (FMCW) radar system for non-contact heart rate monitoring, utilizing high-gain Fabry–Perot cavity (FPC) antennas at both the transmitter and receiver. Each FPC antenna integrates a partially reflective surface (PRS) and a metallic ground plane to form a resonant cavity. Compared to conventional patch arrays of the same aperture, the FPC antenna improves the antenna gain from 4.1 dBi to 8.1 dBi at the transmitter and from 3.9 dBi to 7.8 dBi at the receiver, resulting in an overall link budget enhancement of approximately 7.9 dB. This dual high-gain configuration theoretically increases the maximum detection range by a factor of 2.48. The proposed radar system was implemented and experimentally validated under indoor conditions using both calibration targets and human participants. Active measurement results confirm that the bistatic radar equipped with FPC antennas extends the reliable heart rate detection distance by approximately 2.27 times compared to a conventional system, closely matching the theoretical prediction. These results confirm the practicality and effectiveness of FPC antennas in extending both the range and reliability of millimeter-wave vital sign detection systems. Full article

With the proliferation of the Internet of Things (IoT), gesture recognition has attracted attention as a core technology in human–computer interaction (HCI). In particular, mmWave frequency-modulated continuous-wave (FMCW) radar has emerged as an alternative to vision-based approaches due to its robustness to illumination changes and advantages in privacy. However, in real-world human–machine interface (HMI) environments, hand gestures are inevitably accompanied by torso- and arm-related reflections, which can also contain gesture-relevant variations. To effectively capture these variations without discarding them, we propose a preprocessing method called Adaptive Top-K Selection, which leverages vector entropy to summarize and preserve informative signals from both hand and body reflections. In addition, we present a Multi-Stream EfficientNetV2 architecture that jointly exploits temporal range and Doppler trajectories, together with radar-specific data augmentation and a training optimization strategy. In experiments on the publicly available FMCW gesture dataset released by the Karlsruhe Institute of Technology, the proposed method achieved an average accuracy of 99.5%. These results show that the proposed approach enables accurate and reliable gesture recognition even in realistic HMI environments with co-existing body reflections. Full article

This paper presents a high-sensitivity multi-point seawater temperature detection system based on the virtual Vernier effect, achieved through multiplexed Fabry–Perot (FP) cavities combined with optical frequency-modulated continuous wave (FMCW) interferometry. To address the nonlinear frequency scanning issue inherent in FMCW systems, this paper implemented a software compensation method. This approach enables accurate positioning of multiple FP sub-sensors and effective demodulation of the sensing interference spectrum (SIS) for each FP interferometer (FPI). Through digital signal processing (DSP) algorithms and spectral demodulation, each sub-FP sensor generates an artificial reference spectrum (ARS). The virtual Vernier effect is then achieved by means of a computational process that combines the SIS intensity with the corresponding ARS intensity. This eliminates the need for physical reference arrays with carefully detuned spatial frequencies, as is required in traditional Vernier effect implementations. The sensitivity amplification can be dynamically adjusted with the modulation function parameters. Experimental results demonstrate that an optical fiber link of 82.3 m was achieved with a high spatial resolution of 23.9 $\mathsf{\mu }\mathrm{m}$. Within the temperature range of ${30 }^{\circ }\mathrm{C}$ to ${70 }^{\circ }\mathrm{C}$, the temperature sensitivities of the three enhanced EIS reached −275.56 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, −269.78 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, and −280.67 $\mathrm{pm}{/}^{\circ }\mathrm{C}$, respectively, representing amplification factors of 3.32, 4.93, and 6.13 compared to a single SIS. The presented approach not only enables effective multiplexing and spatial localization of multiple fiber sensors but also successfully amplifies weak signal detection. This breakthrough provides crucial technical support for implementing quasi-distributed optical sensitization sensing in marine environments, opening new possibilities for high-precision oceanographic monitoring. Full article

Frequency-Modulated Continuous-Wave (FMCW) Laser Detection and Ranging (LiDAR) systems are widely used due to their high accuracy and resolution. Nevertheless, conventional distance extraction methods often lack robustness in noisy and complex environments. To address this limitation, we propose a deep learning-based signal extraction framework that integrates a Dual Convolutional Neural Network (DCNN) with a Transformer model. The DCNN extracts multi-scale spatial features through multi-layer and pointwise convolutions, while the Transformer employs a self-attention mechanism to capture global temporal dependencies of the beat-frequency signals. The proposed DCNN–Transformer network is evaluated through beat-frequency signal inversion experiments across distances ranging from 3 m to 40 m. The experimental results show that the method achieves a mean absolute error (MAE) of 4.1 mm and a root-mean-square error (RMSE) of 3.08 mm. These results demonstrate that the proposed approach provides stable and accurate predictions, with strong generalization ability and robustness for FMCW LiDAR systems. Full article

In future 6G systems, integrated sensing and communication (ISAC) in the terahertz (THz) band are emerging as a key technology. Photomixing-based approaches offer advantages for the generation and control of THz waves due to their wide bandwidth and frequency tunability. This paper proposes and experimentally demonstrates a THz-band ISAC system that employs high-speed wavelength tunable lasers. Leveraging the rapid wavelength tunability of the laser, the system simultaneously generates a frequency-modulated continuous-wave (FMCW) radar signal and a frequency-shift keying (FSK) communication signal. Experimental results show successful ranging with a centimeter-level distance measurement error using a 7.9 GHz sweep-bandwidth THz-FMCW signal. The system achieves a short repetition period of 800 ns, significantly enhancing real-time performance in dynamic environments. Moreover, 2FSK communication at 2 Gbit/s was demonstrated without the use of an external modulator, achieving a BER below the HD-FEC threshold. These results confirm that radar and communication functionalities can be integrated into a single transmitter. The proposed system contributes to reducing system complexity and cost and offers a promising solution for 6G applications. Full article

Effective Cardiopulmonary Resuscitation (CPR) requires precise chest compression depth, but current out-of-hospital monitoring technologies face limitations. This study introduces a method using frequency-modulated continuous-wave (FMCW) radar to remotely and accurately monitor chest compressions. FMCW radar captures range, Doppler, and angular data, and we utilize micro-Doppler signatures for detailed motion analysis. By integrating Doppler shifts over time, chest displacement is estimated. We compare a regression model based on maximum Doppler frequency with deep convolutional neural networks (DCNNs) trained on spectrograms generated via short-time Fourier transform (STFT) and the Wigner–Ville distribution (WVD). The regression model achieved a root mean square error (RMSE) of 0.535 cm. The STFT-based DCNN improved accuracy with an RMSE of 0.505 cm, while the WVD-based DCNN achieved the best performance with an RMSE of 0.447 cm, representing an 11.5% improvement over the STFT-based DCNN. These findings highlight the potential of combining FMCW radar and deep learning to provide accurate, real-time chest compression depth measurement during CPR, supporting the development of advanced, non-contact monitoring systems for emergency medical response. Full article

Microwave frequency comb (MFC) optimization for frequency-modulated continuous-wave (FMCW) generation by period-one (P1) dynamics with dual-loop optical feedback are numerically investigated. The linewidth, the side peak suppression (SPS) ratio, and the comb contrast are adopted to quantitatively evaluate the optimization performance, which directly influence the phase stability, spectral purity and repeatability of the MFC. The results show that intensity modulation of the optical injection can generate a sweepable FMCW signal after photodetection via the optical beat effect. When optical feedback loops are introduced, the single-loop configuration can reduce the phase noise of the FMCW signal whereas a dual-loop configuration exploits the Vernier effect to achieve further linewidth reduction and wide tolerance to the feedback strength. Finally, for both the SPS ratio and comb contrast, the dual-loop configuration achieves a higher SPS ratio and maintains high contrast across a wide range of optical feedback loop delays, which outperforms the loop time tolerance of the single-loop configuration. Full article

This paper presents an optimised signal processing framework for contactless physiological monitoring using Frequency Modulated Continuous Wave (FMCW) radar within automotive environments. This research focuses on enhancing heart rate (HR) and heart rate variability (HRV) detection from radar signals by integrating radar placement optimisation and advanced phase-based processing techniques. Optimal radar placement was evaluated through Signal-to-Clutter Ratio (SCR) analysis, conducted with multiple human participants in both laboratory and dynamic driving simulator experimental conditions, to determine the optimal in-vehicle location for signal acquisition. An effective processing pipeline was developed, incorporating background subtraction, range bin selection, bandpass filtering, and phase unwrapping. These techniques facilitated the reliable extraction of inter-beat intervals and heartbeat peaks from the phase signal without the need for contact-based sensors. The framework was evaluated using a Walabot FMCW radar module against ground truth HR signals, demonstrating consistent and repeatable results under baseline and mild motion conditions. In subsequent work, this framework was extended with deep learning methods, where radar-derived HR and HRV were benchmarked against research-grade ECG and achieved over 90% accuracy, further reinforcing the robustness and reliability of the approach. Together, these findings confirm that carefully guided radar positioning and robust signal processing can enable accurate and practical in-cabin physiological monitoring, offering a scalable solution for integration in future intelligent vehicle and driver monitoring systems. Full article

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article