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Millimeter wave radar as a type of noncontact sensor can more conveniently and insensibly obtain breathing and heartbeat signals. However, due to the weak radar echo signal and the influence of noise, there may be phase ambiguity and separation distortion of breathing and heartbeat signals during the processing, which affects the accuracy of detection. In this paper, an enhanced extended differential and cross multiplication (EDACM) algorithm is presented to obtain chest position signals without phase ambiguity. A specific order finite impulse response (FIR) filter with a Blackman window is applied to extract breath signals without waveform distortion from phase signals, and the heartbeat signal without rate distortion is extracted from the phase signal by means of the designed Chebyshev II filter. The measured results show that the proposed method achieves 96.78% accuracy for heart rate detection and 94.44% accuracy for respiratory rate detection, and improves the accuracy of heart rate detection by 2.22% and respiratory rate detection by 4.06% compared to the band-pass filter method under the same parameter conditions. [ABSTRACT FROM AUTHOR]

In the field of millimeter-wave (MMW) imaging, the integration of artificial intelligence (AI) has emerged as a crucial solution for addressing automation challenges. In this study, concealed object classification was successfully achieved on point cloud data from MMW radar high-precision imaging using the PointNet + + deep learning method. The utilized dataset comprises point cloud data generated through the transformation of 3D models and reconstruction of physical objects with an accuracy of less than 1 mm via MMW radar scanning. Classification accuracy was significantly improved by introducing data enhancement techniques, including the generation of homologous data and optimization of sampling points. After several evaluations, 300 epochs of training were conducted using 8192 sampling points, the results showed an accuracy of 0.998 for the training dataset and 0.996 for the test dataset. Moreover, evaluations of samples not included in the original dataset as well as multi-surface scans of concealed objects within the cardboard both resulted in correct predictions, which further validates the effectiveness and reliability of the study and demonstrates the potential of AI applied to MMW imaging. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to measuring eyelid movement using millimeter wave (mmW) radar technology. A two-step method is proposed, involving the observation of a small resolution cell corresponding to the monitored eye and the evaluation of the phase evolution over the measurement period. Simulations are conducted to support radar system optimization and data interpretation with a focus on detecting eyelid movement patterns and compensating for interference from other parts of the body. The feasibility of using this method with eyeglasses is also explored. The proposed technique's advantages and limitations are discussed in comparison with existing measurement alternatives. The characteristics of eyelid dynamics, including blink frequency, regularity, duration, and velocity can be used to assess neurological conditions and driver drowsiness. [ABSTRACT FROM AUTHOR]

With the wide application of millimeter-wave radar signals in medical monitoring, accurately mapping these signals to ECG signals has become a key challenge in meeting the needs for daily continuous non-contact ECG monitoring. The signal processing flow of millimeter-wave radar was introduced in detail, the fine-grained mapping relationship between radar signals and ECG signals was explored, and the nonlinear transformation from radar signals to electrocardiograms was achieved through the introduction of the CAE-BiLSTM deep learning network, which was a hybrid of a convolutional autoencoder (CAE) and bi-directional long short-term memory (BiLSTM), incorporating the convolutional block attention module (CBAM).The results show that the median morphological accuracy of the proposed method is 0.92, and the feature peak prediction error is less than 50 ms. The proposed approach significantly enhances the mapping relationship between radar and ECG signals and offers a new idea for generating non-contact ECG signals.

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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Synthetic Aperture Radar (SAR) has the advantage of noncontact monitoring around the clock and is an important tool for closed space security monitoring. However, when SAR is employed in complex closed spaces, it is susceptible to multipath effects, resulting in a considerable number of virtual images in the image, which has a detrimental impact on interpretation. Existing methods require scene priors for multipath estimation or subaperture weighted fusion to suppress multipath; however, accurately distinguishing multipath virtual images from target images is challenging. This paper proposes a novel multi-angle dual-layer deviation measurement method that effectively distinguishes multipath virtual images from targets. The proposed method employs a large viewing angle difference to conduct multi-angle observation of the target scene, capitalizing on the fact that the position of the multipath virtual image varies with the observation angle, whereas the actual target position remains constant; this is followed by applying a dual-layer deviation measurement algorithm. The algorithm calculates the deviation between the sequence amplitude value and mean twice based on the sparsity of multipath in the multiangle sequence. The proposed method accurately detects and removes sparse and unstable multipath components, whereas the remaining stable components are averaged. This effectively suppresses multipath while retaining target information. Finally, the simulation and actual millimeter wave radar data processing verified the effectiveness of the proposed method.

Focusing on the issue that millimeter-wave radar was difficult to accurately estimate the vital signs of multiple moving targets in complex indoor scenes, a multi-target vital sign estimation method based on sparse representation under complex scenes was proposed. Firstly, the echo data was preprocessed to acquire the point clouds of target and background. After that, a dynamic clutter suppression model was constructed to filter out the dynamic interference. In what follows, the echo data was assigned to the corresponding target, and multi-target tracking could be achieved by exploiting the extended Kalman filter to extract the phase information of the chests of the multi-moving targets. Subsequently, with the sparsity of respiratory and heartbeat signals in the frequency domain, a data-driven adaptive dictionary construction method was proposed to effectively separate respiratory and heartbeat signals. Finally, high precision multi-target vital signs estimation could be achieved by using the sparse reconstruction method. Amount of experimental results in the actual scenes show that the proposed method can effectively perceive the vital signs of multi-target in complex dynamic clutter scenes as compared to the state-of-the-art vital sign estimation methods.

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Noncontact vital signs sensing is the technology for measuring vitals without attaching measuring instruments to the human body. Compared with the contact type, the noncontact type does not require the stress of mounting a measuring instrument. In addition, it is an effective means for patients who have difficulty attaching measurement instruments. This paper focuses on a millimeter-wave (MMW) radar as a device for noncontact vital signs sensing, and we develop a noncontact respiration measurement system using the MMW radar. We have experimentally measured the respiration rate of a subject using the developed system inside and outside the electromagnetic anechoic chamber. As a result, we have confirmed that the measured respiration rate corresponds to the subject-counted respiration rate. Moreover, there was almost no effect of environmental noise on the respiration rate measurement with this study’s experimental environment and parameters. [ABSTRACT FROM AUTHOR]

Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for measuring respiratory movements during swallowing using millimeter wave radar to detect these pauses. The experiment involved 20 healthy adult participants. The results showed a correlation of 0.71 with the measurement data obtained from a band-type sensor used as a reference, demonstrating the potential to measure chest movements associated with respiration using a non-contact method. Additionally, temporary respiratory pauses caused by swallowing were confirmed by the measured data. Furthermore, using machine learning, the presence of respiring alone was detected with an accuracy of 88.5%, which is higher than that reported in previous studies. Respiring and temporary respiratory pauses caused by swallowing were also detected, with a macro-averaged F1 score of 66.4%. Although there is room for improvement in temporary pause detection, this study demonstrates the potential for measuring respiratory movements during swallowing using millimeter wave radar and a machine learning method. [ABSTRACT FROM AUTHOR]

In the process of detecting human respiration and heart rate using millimeter wave radar, the static clutter in the environment makes it extremely difficult for the radar to discriminate the information about the movement of the human chest cavity, which affects the separation of respiration and heart rate signals. At the same time, it is difficult to separate the harmonic components of the respiration signal in the high-frequency band and the heartbeat signal in the low-frequency band because the frequencies of the harmonic components in the high-frequency band are similar.To solve the above problems, this paper proposes a joint modal spatial domain estimation detection method, which mainly adopts an single ensemble empirical modal decomposition (SEEMD） algorithm to decompose the thoracic phase signal into each modal component to eliminate the influence of static noise on the respiratory heartbeat signal, and then uses a multiple signal classification (MUSIC) algorithm to convert the heartbeat modal component signals from the time domain to the spatial domain to estimate their frequencies to eliminate the influence of respiratory harmonics.The experimental results show that the accuracy of respiratory rate under the detection of this paper’s method is 95.76%,and the accuracy of heart rate is 98.76%.Compared with the traditional algorithms, the proposed method is more accurate in estimating respiratory rate and heart rate. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a compact 6 × 8 channel multiple-input multiple-output frequency-modulated continuous-wave radar system capable of determining the three-dimensional positions of targets despite utilizing a linear virtual array. The compact system, containing two cascaded radar transceiver ICs, has 48 virtual channels. We conduct a direction of arrival estimation with these virtual channels to determine the azimuth angle. To overcome the spatial limitation of the linear array, we use frequency-steered transmit antennas, which vary their main lobe direction during the frequency chirp, allowing the elevation angle to be determined by using a sliding window fast Fourier transform algorithm. In this study, we present the system's concept along with the associated signal processing. By taking measurements in different scenarios, each with differently placed corner reflectors, we investigate the capability of the system to separate adjacent targets concerning range, azimuth, and elevation. These measurements are additionally employed to point out the design trade-offs inherent to the system. [ABSTRACT FROM AUTHOR]

A system-in-package for a wideband digital radar, in D-band, requires broadband, high-gain antennas combined with broadband chip-to-package and package-to-printed circuit board (PCB) interconnects. This paper demonstrates a wideband, low-loss quasi-coaxial signal transition, and a novel electric split ring resonator (eSRR)-based antenna-in-package (AiP) with a modified reflector concept, for improved gain, in embedded wafer level ball grid array (eWLB) technology. A complete chip-to-package-to-PCB interconnect is also demonstrated by combining the quasi-coaxial transition with a chip-to-package interconnect. The quasi-coaxial signal transition has the largest impedance bandwidth among ball grid array-based quasi-coaxial signal transitions. For the modified reflector concept, a horn-shaped cavity is micromachined in the PCB substrate and remetallized with aerosol-jet printing, placing the reflector 0.25 λ from the antenna. The antenna gain is improved with up to 5.3 dB. The AiP with the horn-shaped reflector is the single element with the highest gain, in eWLB technology, above 100 GHz. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a compact 6 × 8 channel multiple-input multiple-output frequency-modulated continuous-wave radar system capable of determining the three-dimensional positions of targets despite utilizing a linear virtual array. The compact system, containing two cascaded radar transceiver ICs, has 48 virtual channels. We conduct a direction of arrival estimation with these virtual channels to determine the azimuth angle. To overcome the spatial limitation of the linear array, we use frequency-steered transmit antennas, which vary their main lobe direction during the frequency chirp, allowing the elevation angle to be determined by using a sliding window fast Fourier transform algorithm. In this study, we present the system's concept along with the associated signal processing. By taking measurements in different scenarios, each with differently placed corner reflectors, we investigate the capability of the system to separate adjacent targets concerning range, azimuth, and elevation. These measurements are additionally employed to point out the design trade-offs inherent to the system. [ABSTRACT FROM AUTHOR]

A system-in-package for a wideband digital radar, in D-band, requires broadband, high-gain antennas combined with broadband chip-to-package and package-to-printed circuit board (PCB) interconnects. This paper demonstrates a wideband, low-loss quasi-coaxial signal transition, and a novel electric split ring resonator (eSRR)-based antenna-in-package (AiP) with a modified reflector concept, for improved gain, in embedded wafer level ball grid array (eWLB) technology. A complete chip-to-package-to-PCB interconnect is also demonstrated by combining the quasi-coaxial transition with a chip-to-package interconnect. The quasi-coaxial signal transition has the largest impedance bandwidth among ball grid array-based quasi-coaxial signal transitions. For the modified reflector concept, a horn-shaped cavity is micromachined in the PCB substrate and remetallized with aerosol-jet printing, placing the reflector 0.25 λ from the antenna. The antenna gain is improved with up to 5.3 dB. The AiP with the horn-shaped reflector is the single element with the highest gain, in eWLB technology, above 100 GHz. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

In this manuscript, the problem of assessing the heart rate variability (HRV) of a single subject using a colocated multiple-input multiple-output radar of frequency modulated continuous wave type is investigated. The proposed solution exploits beamforming to acquire multiple measurements from different points on the body of the monitored subject. These measurements are combined using a selection strategy that aims at reducing the impact of random body movements while enhancing the overall signal-to-noise ratio. The resulting signal is then filtered using a physiology-inspired filter. Our results demonstrate that the proposed estimation method can accurately identify the instants at which heartbeats occur and achieve accuracy similar to a regular electrocardiogram in terms of specific HRV metrics referring to interbeat intervals.

Physicians typically measure deep tendon reflexes visually, leading to ambiguity and disagreement over exact reflex classification. Millimeter-wave radar addresses this problem by providing an accurate, unambiguous measurement of reflex limb motion and features noncontact sensing for convenience and patient comfort. Radar spectrograms closely match optical motion capture results, supporting radar’s viability as a clinical assessment tool. This study analyzes data from 60 radar and motion capture measurement trials across four subjects. Six reflex characteristics are defined and extracted. The extracted parameters show a high level of agreement between the two different techniques, with a mean relative error of only 10.39%. Additionally, a positive correlation was observed between hammer tap speed and reflex response speed, with maximum leg velocities showing a slope of 0.4. This study also quantifies and discusses the effects of hammer tap speed and leg length. An analytical model is derived to describe the patellar DTR system dynamics. In the future, physicians may use a specialized radar system to assess reflex performance quickly, accurately, and comfortably for a patient under test.

Traffic monitoring systems featuring robust, multi-sensor fusion capabilities are rapidly growing in demand to observe traffic flow, reduce congestion and to detect and report traffic accidents. However, monitoring outdoor environments using cameras remains challenging due to complex weather conditions, including fog, rain, snow and variable lighting conditions. The presence of these weather conditions can significantly reduce vehicle detection and classification performance using machine learning methods. Unfortunately, openly available datasets for multi-sensor traffic monitoring development and testing remain limited, especially those featuring infrastructure-based cameras and millimeter wave (mmWave) radar. To address these challenges, we evaluate open camera and mmWave radar data using vehicle classification models for cars, trucks, vans and buses on embedded hardware. We also provide an open multi-sensor traffic monitoring dataset with more than 8,000 manually annotated frames as well as mmWave radar point clouds recorded in an urban environment under sunny, partially cloudy, cloudy, rainy and night conditions.

Single snapshot forward-looking imaging technology with high performance and resolution is crucial for enabling the development of automotive radars. However, range migration issues can limit the implementation of coherent integration methods, and improving system resolution is generally difficult due to hardware parameter limitations. Based on the Time-Division Multiplexing Multiple-Input-Multiple-Output (TDM-MIMO) forward-looking imaging systems of automotive millimeter wave radar, this paper proposes Doppler domain compensation and point-to-point echo correction measures for achieving multidomain signal decoupling. However, the accuracy of traditional single-dimension range and angle imaging is limited by the number of finite array elements and significant noise interference. Therefore, this paper proposes a multidomain joint estimation algorithm based on the Improved Bayesian Matching Pursuit (IBMP) method. The Bayesian method is based on the Bernoulli-Gaussian (BG) model, and the estimated parameters and support domain are iteratively updated in this method while adhering to the Maximum a Posteriori (MAP) criterion constraint to achieve the high-precision reconstruction of multidimensional joint signals. The final set of simulation and actual measurement results demonstrate that the proposed method can effectively solve the problem of range migration and improve the angle resolution of radar forward-looking imaging while exhibiting excellent noise robustness.

The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%. [ABSTRACT FROM AUTHOR]

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown. [ABSTRACT FROM AUTHOR]

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario's radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data. [ABSTRACT FROM AUTHOR]

To position the personnel in railway tunnels in real time and ensure safe construction, a railway tunnel personnel positioning technology based on millimeter wave radar is proposed. Considering the characteristics of railway tunnel construction environment, a frequency-modulated continuous wave radar is used. First, the distance, velocity, and angle of the target point are measured by analyzing the frequency, time delay, and phase of the radar reflection wave, and the clutter elimination and target detection algorithm are used to obtain the point cloud of the target. Then, a target tracking algorithm is used to cluster and track the point cloud, obtaining and displaying the position, velocity, and trajectory of the personnel, and issuing alarms in a timely manner for abnormal behaviors. Finally, a semi-physical simulation environment is constructed. Radar nodes are deployed inside the tunnel, and processing servers are deployed outside the tunnel to experimentally verify the proposed technical solution. The research results show that a single radar node can simultaneously track the position distribution and posture information of at least six targets. The technical solution can meet the requirements of real-time and precise positioning of personnel in railway tunnels. [ABSTRACT FROM AUTHOR]

To address challenges posed by long braking distances, long perception distances, and difficulty in constructing clearance ahead of running trains in real time, a millimeter-wave radar-based long-range perception system is proposed to detect track boundaries and train contours. This system is equipped with a set of high-resolution long-range millimeter-wave radars that are adaptable to the strong scattering environment of rail transit and features an accurate method of calculating train speed based on the region of interest and the continuity hypothesis of speed. By comparing with the train speed, radar detection targets are classified into two categories: dynamic targets and static targets. Based on this classification, feature points of track clearance are extracted in a stable manner and boundary curve fitting is achieved using the seed method. In addition, an improved density-based spatial clustering of applications with noise (DBSCAN) method is proposed, using location and speed as clustering parameters and with different weights assigned to varying dimensions, to identify train contours by clustering and shape estimation based on the target track information output by radar, and assess collision risks according to the boundary information. Results from the on-board test demonstrate that the proposed system can realize stable fitting of the track boundary ahead and train contour within a range of 400 meters, which increases the detection distance by four times compared to that of traditional car radars, and enables collision warning to trains that may be involved in clearance invasion, thus helping avoid the occurrence of accidents.