Millimeter-wave Radar Research Articles

A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing

Wind power is a crucial direction for new energy transition technology in response to the challenges of global warming. However, the potential for collisions between the blades and the tower barrel remains a significant concern. To address this issue, a large number of sensors, such as lasers and cameras, are attached to the structure, but they struggle to operate in complex weather and at night. This paper presents a method of employing a 79 GHz FMCW (frequency-modulated continuous wave) mmWave (millimeter-wave) radar with circularly polarization on the top of the tower. During the design, two main considerations are raised: (1) Since the small-RCS (radar cross-section) blade experiences an oblique incidence from more than 70 m away, the channel SNR (signal-to-noise ratio) is low, so high-gain antennas and SIMO (single-input multiple-output) radar configurations are designed to increase the Pt (transmitting power). (2) Wind turbines are often located in offshore or mountainous areas with a high level of weather interference, so a pair of circularly polarized antenna is used to reduce the interference of meteorological particles to the radar. Finally, test results from a practical wind turbine in different weather conditions prove its practicality. During tests, the wind turbine operates at a rotor speed of 6 to 12 rounds per minute, and the clearance range has an obvious inverse relationship with it, ranging from 6 to 12 m. This technology enhances safety, maximizes efficiency, and enables optimal length and weight determination during design for improved power generation.

Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods

In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents.

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar.

Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.

Addressing Specularity: Millimeter-Wave Radar With Distributed Repeater Apertures

Addressing Specularity: Millimeter-Wave Radar With Distributed Repeater Apertures

Application of nonlinear signal analysis in millimeter-wave radar blood flow rate monitoring

Abstract Due to the advantages of millimeter-wave radar, such as its relatively simple structure, small size, high sensitivity, etc., it is used to obtain the human pulse signal and blood flow signal. Using the methods of mean cancellation, phase differencing, and smoothing, static clutter removal and heartbeat signal enhancement can be achieved, which can effectively eliminate interference information. Based on the collected information, the three-dimensional Navier-Stokes equations and boundary conditions are determined, and the theoretical knowledge related to fluid mechanics and bioengineering is used to construct the model of the multidimensional blood monitoring system, and the nonlinear signal analysis method is used to analyze the application of the constructed model. Based on the data, there is only a 0.1% difference between the waveform of the blood vessel diameter measured by the multidimensional blood monitoring system and that of the blood vessel diameter tested by commercial ultrasound equipment. The waveform trend is also consistent. In addition, the blood flow velocity at point 5 was much greater than that at point 2, when the vessel stenosis exceeded 50%. This study can accurately and efficiently monitor blood flow velocity for single and multiple individuals, which is critical for the healthcare field's development and innovation.

Metaconductor Based Integrated Array Antenna for High Efficiency Millimeter-Wave Wireless Communications and Radar Applications

Metaconductor Based Integrated Array Antenna for High Efficiency Millimeter-Wave Wireless Communications and Radar Applications

Wireless Reading and Localization of Additively Manufactured Galinstan-Based Sensor Using a Polarimetric Millimeter-Wave Radar Imaging Technique

Wireless Reading and Localization of Additively Manufactured Galinstan-Based Sensor Using a Polarimetric Millimeter-Wave Radar Imaging Technique

Multi-Modal Fusion Sensing: A Comprehensive Review of Millimeter-Wave Radar and Its Integration With Other Modalities

Multi-Modal Fusion Sensing: A Comprehensive Review of Millimeter-Wave Radar and Its Integration With Other Modalities

An adaptive algorithm for generating 3D point clouds of the human body based on 4D millimeter-wave radar.

The traditional algorithms for generating 3D human point clouds often face challenges in dealing with issues such as phantom targets and target classification caused by electromagnetic multipath effects, resulting in a lack of accuracy in the generated point clouds and requiring manual labeling of the position of the human body. To address these problems, this paper proposes an adaptive method for generating 3D human point clouds based on 4D millimeter-wave radar (Self-Adaptive mPoint, SA-mPoint). This method estimates the rough human point cloud by considering micro-motion and respiration characteristics while combining the echo dynamic with static information. Furthermore, it enhances the density of point cloud generation. It reduces interference from multipath noise through multi-frame dynamic fusion and an adaptive density-based clustering algorithm based on the center points of humans. The effectiveness of the SA-mPoint algorithm is verified through experiments conducted using the TI Millimeter Wave Cascade Imaging Radar Radio Frequency Evaluation Module 77G 4D cascade radar to collect challenging raw data consisting of single-target and multi-target human poses in an open classroom setting. Experimental results demonstrate that the proposed algorithm achieves an average accuracy rate of 97.94% for generating point clouds. Compared to the popular TI-mPoint algorithm, it generates a higher number of point clouds on average (increased by 87.94%), improves the average accuracy rate for generating point clouds (increased by 78.3%), and reduces the running time on average (reduced by 11.41%). This approach exhibits high practicality and promising application prospects.

Quantitative Assessment of Fall Risk in the Elderly Through Fusion of Millimeter-Wave Radar Imaging and Trajectory Features

Quantitative Assessment of Fall Risk in the Elderly Through Fusion of Millimeter-Wave Radar Imaging and Trajectory Features

RPM 2.0: RF-Based Pose Machines for Multi-Person 3D Pose Estimation

Advanced human sensing technologies based on radio frequency (RF) signals have gained widespread attention in recent years. However, due to the sparsity and incompleteness of RF signals, fine-grained RF-based multi-person 3D pose estimation has progressed more slowly. In this paper, we present RF-based Pose Machine (RPM 2.0) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we treat the horizontal and vertical millimeter-wave radars as “RF cameras” with different viewing angles and propose a Multi-view Fusion Network to unproject the RF signals into a unified latent feature space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RPM 2.0 and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RPM 2.0 outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of 73 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> and generalizes well in new data such as occlusion, low illumination.

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds.

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human-computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

4D RADAR/IMU/GNSS INTEGRATED POSITIONING AND MAPPING FOR LARGE-SCALE ENVIRONMENTS

Abstract. 4D millimeter-wave radar can work in harsh weather conditions such as fog and snow, and measure the position and radial Doppler velocity of objects in three-dimensional space. Some existing methods perform positioning by fusing 3D velocity or point cloud matching information estimated by 4D radar with IMU information. However, the sparsity of radar point clouds and the interference of large amount of noise lead to low accuracy of odometry, and most of the existing work cannot achieve the global consistent mapping with radar point clouds. In this paper, a 4D radar/IMU/GNSS localization and mapping method, G-iRIOM, is proposed. We tightly coupled IMU measurement, Doppler observation information and point cloud matching information from 4D radar by iterative extended Kalman filtering (iEKF) method, and introduced GNSS RTK and loop closure information as global observation to correct the positioning drift of the odometry. The experimental results show that the tightly coupled Doppler velocity information can effectively improve the control of the pose on the local point cloud position, thus enhancing the mapping accuracy. Meanwhile, the introduction of GNSS and loop closure information can significantly improve the positioning accuracy of 4D radar odometry as a kind of global observation, and achieve global consistent mapping of large-scale outdoor scenes based on 4D radar point clouds.

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

Machine learning‐based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures

AbstractThe authors present multiple machine learning‐based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W‐band radars operating at 77 and 94 GHz and a G‐band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi‐class and 2‐class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi‐class classification. For the spectrogram feature‐based learning, the validation and test accuracy for multi‐class increased to 93.3% and 88.7% respectively. For the 2‐class classification, the spectrum feature‐based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature‐based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross‐validation accuracy has shown very good agreement with the expected anomaly detection rate.

Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements

Abstract. Cloud remains one of the largest uncertainties in weather and climate research due to the lack of fine-resolution observations of cloud vertical structure (CVS) on a large scale. In this study, near-global CVS is characterized by high-vertical-resolution twice-daily radiosonde observations from 374 stations over land, which are distributed in Europe, North America, East Asia, Australia, the Pacific Ocean, and Antarctica. To this end, we initially develop a novel method to determine CVS, by combining both the vertical gradients of air temperature and relative humidity (RH) and the altitude-dependent thresholds of RH. It is found that the cloud base heights (CBHs) from radiosondes have a higher correlation coefficient (R= 0.91) with the CBHs from a millimeter-wave cloud radar than those from the ERA5 reanalysis (R= 0.49). Overall, cloudy skies occur 65.3 % (69.5 %) of the time, of which 55.4 % (53.8 %) are one-layer clouds at 00:00 (12:00) UTC. Most multi-layer clouds are two-layer clouds, accounting for 62.2 % (61.1 %) among multi-layer clouds at 00:00 (12:00) UTC. Geographically, one-layer clouds tend to occur over arid regions, whereas two-layer clouds do not show any clear spatial preference. The cloud bases and tops over arid regions are higher compared with humid regions albeit with smaller cloud thickness (CT). Clouds tend to have lower bases and thinner layer thicknesses as the number of cloud layer increases. The global-mean CT, CBH, and cloud top height (CTH) are 4.89 ± 1.36 (5.37 ± 1.58), 3.15 ± 1.15 (3.07 ± 1.06), and 8.04 ± 1.60 (8.44 ± 1.52) km above ground level (a.g.l.) at 00:00 (12:00) UTC, respectively. The occurrence frequency of clouds is bimodal, with lower peaks between 0.5 and 3 km a.g.l. and upper peaks between 6 and 10 km a.g.l. The CBH, CTH, and CT undergo almost the same seasonality; namely, their magnitudes in boreal summer are greater than in boreal winter. As expected, the occurrence frequencies of clouds exhibit pronounced diurnal cycles in different seasons. In boreal summer, clouds tend to form as the sun rises and the occurrence frequencies increase from morning to late afternoon, with the peak in the early afternoon at the altitude of 6–12 km a.g.l., while in boreal winter, clouds have peak occurrence frequencies in the morning. The relations between surface meteorological variables and moisture with CBH are investigated as well, showing that CBHs are generally more significantly correlated with 2 m relative humidity (RH2 m) and 2 m air temperature (T2 m) than with surface pressure and 10 m wind speed. Larger T2 m and smaller RH2 m always correspond to higher CBH. In most cases CBHs are negatively correlated to soil water content. The near-global CVS obtained from high-vertical-resolution radiosondes in this study can provide key data support for improving the accuracy of cloud radiative forcing simulation in climate models.

Investigation on the Utilization of Millimeter-Wave Radars for Ocean Wave Monitoring

The feasibility of using millimeter-wave radars for wave observations was investigated in this study. The radars used in this study operate at a center frequency of 77.572 GHz. To investigate the feasibility of wave observations and extract one-dimensional and two-dimensional wave spectra, arrays consisting of multiple radar units were deployed for observations in both laboratory and field environments. Based on the data measured with the millimeter-wave radars, one-dimensional wave spectra and two-dimensional wave directional spectra were evaluated using the periodogram method and the Bayesian directional spectrum estimation method (BDM), respectively. Meanwhile, wave parameters such as the significant wave height, wave period, and wave direction were also calculated. Via comparative experiments with a capacitive wave height meter in a wave tank and RADAC’s WG5-HT-CP radar in an offshore field, the viability of using millimeter-wave radars to observe water waves was validated. The results indicate that the one-dimensional wave spectra measured with the millimeter-wave radars were consistent with those measured with the mature commercial capacitive wave height meter and the WG5-HT-CP wave radar. Via wave direction measurement experiments conducted in a wave tank and offshore environment, it is evident that the wave directions retrieved with the millimeter-wave radars were in good alignment with the actual wave directions.

New Approach Based on Pix2Pix–YOLOv7 mmWave Radar for Target Detection and Classification

Frequency modulated continuous wave (FMCW) radar is increasingly used for various detection and classification applications in different fields, such as autonomous vehicles and mining fields. Our objective is to increase the classification accuracy of objects detected using millimeter-wave radar. We have developed an approach based on millimeter-wave radar. The proposed solution combines the use of an FMCW radar, a YOLOv7 model, and the Pix2Pix architecture. The latter architecture was used to reduce noise in the heatmaps. We create a dataset of 4125 heatmaps annotated with five different object classes. To evaluate the proposed approach, 14 different models were trained using the annotated heatmap dataset. In the initial experiment, we compared the models using metrics such as mean average precision (mAP), precision, and recall. The results showed that the proposed model of YOLOv7 (YOLOv7-PM) was the most efficient in terms of mAP_0.5, which reached 90.1%, and achieved a mAP_0.5:0.95 of 49.51%. In the second experiment, we compared the models with a cleaned dataset generated using the Pix2Pix architecture. As a result, we observed improved performances, with the Pix2Pix + YOLOv7-PM model achieving the best mAP_0.5, reaching 91.82%, and a mAP_0.5:0.95 of 52.59%.

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people’s privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body’s when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.