Millimeter-wave Radar Research Articles

Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique

Velocity aliasing is unavoidable for millimeter-wave cloud radar (MMCR) due to its short wavelength. In the vertically pointing MMCR, a special aliasing state called half-folding will cause the traditional postprocessing dealiasing methods used for weather radar, including the dual-PRF method, to fail. In this paper, we propose a method that applies the dual-PRF technique to spectral dealiasing. By utilizing the property that the true velocity difference between peaks should be the same in both PRFs, our method is able to solve a special case of half-folding caused by multiple peaks, which is ignored by other spectral dealiasing methods. The special case, which we call implicit half-folding, occurs in the presence of multiple peaks in a Doppler power spectrum, where none of the peaks are folded, and they appear to be in the same Nyquist interval, whereas the peaks are actually not in the same Nyquist interval. Observations from a 94 GHz vertically pointing MMCR called TJ-II were used to demonstrate various aliasing cases, including the implicit half-folding case. As a result, our method successfully solved all aliasing cases while the other method failed when the implicit half-folding case occurred.

Three-Dimensional Structure Measurement for Potted Plant Based on Millimeter-Wave Radar

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation.

Mask-VRDet: A robust riverway panoptic perception model based on dual graph fusion of vision and 4D mmWave radar

With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant to autonomous navigation. RGB cameras can capture images with rich semantic features, but they would fail in adverse weather and at night. As a perception sensor that has initially emerged in recent years, 4D millimeter-wave radar (4D mmWave radar) can work in all weather and has more abundant point-cloud features than ordinary radar, but it also suffers from water-surface clutter seriously. Furthermore, the shape and outline of dense point cloud captured by 4D mmWave radar are irregular. CNN-based neural networks treat features as 2D rectangle grids, which excessively favor image modality and are unfriendly to radar modality. Therefore, we transform both features of image and radar into non-Euclidean space as graph structures. In this paper, we focus on robust panoptic perception in inland waterways. Firstly, we propose the first Clutter-Point-Removal (CPR) algorithm for 4D mmWave radar, removing water-surface clutter and improving the recall of radar targets. Secondly, we propose a high-performance panoptic perception model based on the graph neural network called Mask-VRDet, fusing features of vision and radar to simultaneously perform object detection and semantic segmentation. To the best of our knowledge, Mask-VRDet is the first riverway panoptic perception model based on vision-radar graphical fusion. It outperforms other single-modal and fusion models, and achieves state-of-the-art performance on our collected dataset. We release our code at https://github.com/GuanRunwei/Mask-VRDet-Official.

DGSCR: Double-Target Gesture Separation and Classification Recognition Based on Deep Learning and Millimeter-Wave Radar

DGSCR: Double-Target Gesture Separation and Classification Recognition Based on Deep Learning and Millimeter-Wave Radar

Noncontact Measurement of Heartbeat of Humans and Chimpanzees Using Millimeter-Wave Radar With Topology Method

Noncontact Measurement of Heartbeat of Humans and Chimpanzees Using Millimeter-Wave Radar With Topology Method

A Low-Power V-Band Radar Transceiver Front-End Chip Using 1.5 V Supply in 130-nm SiGe BiCMOS

Energy-efficient, low-voltage, and low-power millimeter-wave (mm-wave) radars are gaining increasing attention for battery-powered commercial applications. In this article, the design of a low-power <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> -band radar sensor based on a transceiver (TRX) front-end chip using 1.5 V supply in an advanced SiGe BiCMOS technology with 300 GHz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{f}\textsubscript{T}$</tex-math> </inline-formula> and 500 GHz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{f}\textsubscript{max}$</tex-math> </inline-formula> is presented. The monostatic front-end chip utilizes low-voltage low-power circuit-level design techniques to achieve measured 9-dBm transmitter (TX) output power and 27-dB receiver (RX) gain with a simulated 3.8-dB noise figure (NF) consuming a total of only 72 mW in continuous mode. The TRX chip is used to build a radar sensor, which is experimentally verified in an anechoic chamber. The low-power sensor achieves a 46-dB dynamic range and a ranging precision better than 3.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m measured with a static target at 1 m. Phase measurements using the low-power radar in the continuous-wave (CW) mode demonstrate that submillimeter movements can be tracked, and notably main vital parameters of a human can be determined accurately. Experimental results show that the performance of the proposed low-power TRX front-end chip is very competitive with designs in modern CMOS technologies.

Mutual Interference Mitigation for Automotive FMCW Radar With Time and Frequency Domain Decomposition

Currently, the frequency-modulated continuous-wave (FMCW) millimeter-wave (MMW) radar is a typical choice for automotive and transportation radar systems. As the number of FMCW radars explodes in the current vehicle market and the working frequency is limited in an open window of 76–81 GHz, FMCW radars on the road easily mutually interfere with each other, especially due to their wide bandwidth. Hence, in this article, we rigorously analyze the target echo, i.e., the beat signal, especially the sparsity of the interference signal in the time domain, and the row sparsity of the useful echo signal in the frequency domain. By taking advantage of this feature, we design an interference mitigation optimization problem to extract the target echoes with a row-sparse constraint. A closed-form solution is given in each iteration with specific derivations. Finally, numerical simulations and multiple practical scenes are provided to demonstrate the effectiveness of the proposed method.

Enhancing concealed object detection in Active Millimeter Wave Images using wavelet transform

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches.

Radar signatures of sea lions at K‐band and W‐band

AbstractThe millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported.

DCS-CTN: Subtle Gesture Recognition Based on TD-CNN-Transformer via Millimeter-Wave Radar

Gesture recognition has been a hot research topic in human-computer interaction, since contactless gesture recognition will provide increasing applications in many fields. Millimeter-wave (mmWave) radar well serves this technology because of its high accuracy, easy integration, and strong anti-jamming ability in moving object detection. However, it is still challenging to meet the requirement of high precision in subtle gesture recognition based on traditional methods via point cloud or Range-Doppler heat map of millimeter-wave radar. Considering the raw data from millimeter-wave radar with more information such as phase, we propose a system that uses the constructed millimeter-wave radar data cube sequence and Timedistributed-CNN-Transformer network (CTN), called DCS-CTN system, to get higher hand gesture recognition accuracy. In this system, we introduce a time-distributed wrapper (TD) and convolution neural network (CNN) to extract local features of the data cube sequence, a position encoder to retain time information of the sequence, and a transformer network to get global features of the sequence. The experiments results show that this system can achieve hand gesture recognition accuracy of 99.75%, which is significantly higher than the other traditional approaches.

Closet In-Path Vehicle Detection and Recognition Algorithm Based on Camera and Millimeter-Wave Radar Fusion

&lt;div&gt;The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm. Then, the CIPV recognition algorithm generates virtual lane lines using the motion parameters of self-vehicle or the driving trajectory of vehicle target and develops a mode switch strategy for virtual lane lines generation based on the driving state of target. This strategy can flexibly switch to the applicable virtual lane lines generation method in different scenarios. Finally, field tests are conducted in typical scenarios to verify the performance of the CIPV recognition algorithm. The results show that the algorithm is able to recognize CIPV stably and accurately without relying on road lane lines.&lt;/div&gt;

MmStress

Long-term exposure to stress hurts human's mental and even physical health,and stress monitoring is of increasing significance in the prevention, diagnosis, and management of mental illness and chronic disease. However, current stress monitoring methods are either burdensome or intrusive, which hinders their widespread usage in practice. In this paper, we propose mmStress, a contact-less and non-intrusive solution, which adopts a millimeter-wave radar to sense a subject's activities of daily living, from which it distills human stress. mmStress is built upon the psychologically-validated relationship between human stress and "displacement activities", i.e., subjects under stress unconsciously perform fidgeting behaviors like scratching, wandering around, tapping foot, etc. Despite the conceptual simplicity, to realize mmStress, the key challenge lies in how to identify and quantify the latent displacement activities autonomously, as they are usually transitory and submerged in normal daily activities, and also exhibit high variation across different subjects. To address these challenges, we custom-design a neural network that learns human activities from both macro and micro timescales and exploits the continuity of human activities to extract features of abnormal displacement activities accurately. Moreover, we also address the unbalance stress distribution issue by incorporating a post-hoc logit adjustment procedure during model training. We prototype, deploy and evaluate mmStress in ten volunteers' apartments for over four weeks, and the results show that mmStress achieves a promising accuracy of ~80% in classifying low, medium and high stress. In particular, mmStress manifests advantages, particularly under free human movement scenarios, which advances the state-of-the-art that focuses on stress monitoring in quasi-static scenarios.

HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.

2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar

Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar.

Monitoring of sow postural activity from 3D millimeter-wave radar imaging

This paper presents a novel approach to monitor the postural activity of sows in farrowing pen using a millimeter-wave radar imaging system. Three-dimensional images of the scene are obtained from a 77 GHz Multiple-Input Multiple Output radar and the mechanical scanning of the radar beam. We show that the processing of radar images based on the Cell-Averaging Constant False Alarm Rate allows detecting the standing and lying postures of sows despite the electromagnetic clutter. The experimental study reported here is performed from the monitoring of 16 sows from the Large White breed for a total of 46 h. 3D positions of the sow are recorded and heatmaps of the sow locomotor activity are proposed. With few training radar data, we perform a classification of standing and laying postures with a mean balanced accuracy greater than 90%. The radar-based technique applied here in a cluttered environment inside farrowing pens may be extended to monitor pigs under other housing condition.

MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals.

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage-it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time.

Underwater Target Detection by Measuring Water-Surface Vibration With Millimeter-Wave Radar

Underwater target detection has been a significant challenge in environment aware. In this letter, an underwater target detection scheme is proposed by measuring the water-surface vibrations caused by acoustic waves with a millimeter-wave radar. Firstly, the power of the water-surface vibration due to the reflection of acoustic waves by the target is derived, which gives a quantitative relationship between the perturbations on water-surface vibration and the position of the target. Then, the signal model to accurately measure the water-surface vibration with millimeter-wave radar is established. A method to estimate the power spectrum of the phase of water-surface vibrations is proposed, based on which a test statistic for underwater target detection is formulated. Finally, the effectiveness of the proposed scheme is validated by water-tank experiments using a frequency modulated radar with a center frequency of 77 GHz.

A Novel Multiperson Activity Recognition Algorithm Based on Point Clouds Measured by Millimeter-Wave MIMO Radar

Radar is widely used in human activity recognition (HAR). The radar-based HAR algorithms can be roughly categorized into the feature map-based and the point cloud-based. Between the two, the point cloud-based algorithms are more suitable for multi-person activity recognition (MPAR) tasks due to the spatial properties of point clouds, and are therefore increasingly attracting attentions. However, most existing point cloud-based algorithms make decisions by utilizing either Doppler or coordinate features of point clouds, which are not sufficient to characterize the activity; the remaining algorithms have a large randomness in extracting both features. Neither of these algorithms can fully exploit the advantage of feature fusion, which is the key to recognition performance. To address this shortcoming, this paper proposes a novel MPAR algorithm. First, a feature mapping approach is proposed and defined in equation form, the Doppler, range, azimuth, and elevation features of the point clouds are calculated and accumulated sequentially, so we can obtain the four time-domain feature maps. Second, with the four feature maps as inputs, a four-channel CNN classification model with channel attention is trained for MPAR tasks. Datasets of multi-person activities are collected respectively under the indoor circumstance and aquatic circumstance using the millimeter-wave multiple-input-multiple-output (MIMO) radar platform. The dataset-based evaluation performance shows that the proposed algorithm achieves accuracy results of 96.09% for the indoor-MPAR task and 93.97% for the aquatic-MPAR task, and outperforms three conventional point cloud-based algorithms in terms of the overall MPAR accuracy, the generalization ability to the aquatic activity recognition, and the robustness of distance and headcount.

IDT: An Integration of Detection and Tracking Toward Low-Observable Multipedestrian for Urban Autonomous Driving

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident prevention. However, it is a challenging task in urban autonomous driving, since the weak backscattered signals from pedestrians with small radar cross-section may be submerged in strong background clutters, especially under adverse weather conditions. On this account, this article presents an integration of detection and tracking (iDT) toward multipedestrian with a low signal-to-noise ratio (SNR). In particular, in contrast to conventional methods, in which the detection and tracking are treated as two separate processes, we address them jointly to ensure the accuracy of continuous detection and tracking in low SNR conditions. Another distinguishing element is that to accommodate the time-varying number of targets, the Bayesian framework is tailored by augmenting the state vector with a multipedestrian evolutional indicator. The advantage is that all targets can be tracked simultaneously by searching the global likelihood ratio of a spectrum once, rather than assigning an individual tracker to each target in conventional methods. Furthermore, through the proposed integrated framework, the data association problem is circumvented because there is no explicit measurement-target assignment process in our approach. In addition, a commercial automotive multiple-input-multiple-output millimeter-wave radar sensor is employed to validate the proposed method. Consequently, numerous simulation and experiment results turn out that iDT shows unique advantages in low-observable multipedestrian tracking compared with traditional methods.

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results.

As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.