This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range-azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range-azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%.

Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

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.

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%.

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.

Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.

This paper proposes a deep learning-based mmWave radar and RGB camera sensor early fusion method for object detection and tracking and its embedded system realization for ADAS applications. The proposed system can be used not only in ADAS systems but also to be applied to smart Road Side Units (RSU) in transportation systems to monitor real-time traffic flow and warn road users of probable dangerous situations. As the signals of mmWave radar are less affected by bad weather and lighting such as cloudy, sunny, snowy, night-light, and rainy days, it can work efficiently in both normal and adverse conditions. Compared to using an RGB camera alone for object detection and tracking, the early fusion of the mmWave radar and RGB camera technology can make up for the poor performance of the RGB camera when it fails due to bad weather and/or lighting conditions. The proposed method combines the features of radar and RGB cameras and directly outputs the results from an end-to-end trained deep neural network. Additionally, the complexity of the overall system is also reduced such that the proposed method can be implemented on PCs as well as on embedded systems like NVIDIA Jetson Xavier at 17.39 fps.