mmWave Radar Sensor Research Articles

CMOS IC Solutions for the 77 GHz Radar Sensor in Automotive Applications

This paper presents recent results on CMOS integrated circuits for automotive radar sensor applications in the 77 GHz frequency band. It is well demonstrated that nano-scale CMOS technologies are the best solution for the implementation of low-cost and high-performance mm-wave radar sensors since they provide high integration level besides supporting high-speed digital processing. The present work is mainly focused on the RF front-end and summarizes the most stringent requirements of both short/medium- and long-range radar applications. After a brief introduction of the adopted technology, the paper addresses the critical building blocks of the receiver and transmitter chain while discussing crucial design aspects to meet the final performance. Specifically, effective circuit topologies are presented, which concern mixer, variable-gain amplifier, and filter for the receiver, as well as frequency doubler and power amplifier for the transmitter. Moreover, a voltage-controlled oscillator for a PLL efficiently covering the two radar bands is described. Finally, the circuit description is accompanied by experimental results of an integrated implementation in a 28 nm fully depleted silicon-on-insulator CMOS technology.

A Novel Movable Mannequin Platform for Evaluating and Optimising mmWave Radar Sensor for Indoor Crowd Evacuation Monitoring Applications

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane.

TRANS-CNN-Based Gesture Recognition for mmWave Radar.

In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.

Applying mmWave Radar Sensors to Vocabulary-Level Dynamic Chinese Sign Language Recognition for the Community With Deafness and Hearing Loss

Applying mmWave Radar Sensors to Vocabulary-Level Dynamic Chinese Sign Language Recognition for the Community With Deafness and Hearing Loss

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review.

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

Water Surface Acoustic Wave Detection by a Millimeter Wave Radar

Feature extraction and recognition of underwater targets are important in military and civilian areas. This paper studied water surface acoustic wave (WSAW) detection by a millimeter wave (mmWave) radar. The mmWave-based endpoint detection method of the WSAW was introduced. Simulated results show that the continuous wavelet transform (CWT) method has a better detection performance. A 77 GHz large aperture antenna mmWave radar sensor and an underwater acoustic transmitter have been applied to conduct laboratory experiments. Still water surface experimental results verify that the CWT method has better detection capability, and the mmWave radar can accurately detect even 155 nm WSAW. Wavy water surface experimental results demonstrate the ability of the mmWave radar to analyze the time-frequency feature of the weak WSAW signal. These works indicate the potential of mmWave radar for the cross-medium detection and recognition of underwater targets.

Novel Robust On-Line Indoor Occupancy Counting System Using mmWave Radar

In this work, a novel robust on-line indoor occupancy counting approach is proposed using the millimeter wave (mmWave) frequency-modulated continuous-wave (FMCW) radar. The acquired radar data can be represented as sparse three-dimensional (3D) radar point-clouds. The conventional indoor occupancy-counting schemes suffer from various types of errors and uncertainties emerging from mmWave radar sensors. Thus, we propose a novel feature-extraction strategy to obtain the robust features for training the machine-learning driven occupancy-counter. We formulate the underlying indoor occupancy-counting problem as the multi-classification problem such that the k nearest neighbor (KNN) classifier is adopted to identify the number of occupants on line. Experimental results from realworld data demonstrate that our proposed new approach leads to a promising classification-accuracy of 95.8% for indoor occupancy counting. In the comparative study based on the realworld data, our proposed novel indoor occupancy-counting method greatly outperforms other existing schemes.

Heterogeneous Fusion of Camera and mmWave Radar Sensor of Optimizing Convolutional Neural Networks for Parking Meter System.

In this article, a novel heterogeneous fusion of convolutional neural networks that combined an RGB camera and an active mmWave radar sensor for the smart parking meter is proposed. In general, the parking fee collector on the street outdoor surroundings by traffic flows, shadows, and reflections makes it an exceedingly tough task to identify street parking regions. The proposed heterogeneous fusion convolutional neural networks combine an active radar sensor and image input with specific geometric area, allowing them to detect the parking region against different tough conditions such as rain, fog, dust, snow, glare, and traffic flow. They use convolutional neural networks to acquire output results along with the individual training and fusion of RGB camera and mmWave radar data. To achieve real-time performance, the proposed algorithm has been implemented on a GPU-accelerated embedded platform Jetson Nano with a heterogeneous hardware acceleration methodology. The experimental results exhibit that the accuracy of the heterogeneous fusion method can reach up to 99.33% on average.

Application of mmWave Radar Sensor for People Identification and Classification.

Device-free indoor identification of people with high accuracy is the key to providing personalized services. Visual methods are the solution but they require a clear view and good lighting conditions. Additionally, the intrusive nature leads to privacy concerns. A robust identification and classification system using the mmWave radar and an improved density-based clustering algorithm along with LSTM are proposed in this paper. The system leverages mmWave radar technology to overcome challenges posed by varying environmental conditions on object detection and recognition. The point cloud data are processed using a refined density-based clustering algorithm to extract ground truth in a 3D space accurately. A bi-directional LSTM network is employed for individual user identification and intruder detection. The system achieved an overall identification accuracy of 93.9% and an intruder detection rate of 82.87% for groups of 10 individuals, demonstrating its effectiveness.

MmWave Radar Sensors Fusion for Indoor Object Detection and Tracking

Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is critical to achieving an efficient tracking system. This paper uses two mmWave radar sensors for accurate object detection and tracking: two noise reduction stages to reduce noise and distinguish cluster groups. The presented data fusion method effectively estimates the transformation of the data alignment and synchronizes the result that can allow us to visualize the objects’ information acquired by one radar on another one. An efficient density-based clustering algorithm to provide high clustering accuracy is presented. The Unscented Kalman Filter tracking algorithm with data association tracks multiple objects simultaneously in terms of accuracy and timing. Furthermore, an indoor object tracking system is developed based on our proposed method. Finally, the proposed method is validated by comparing it with our previous system and a commercial system. The experimental results demonstrate that the proposed method’s advantage is of positive significance for handling the effect of occlusions at higher numbers of weak data and for detecting and tracking each object more accurately.

High-Efficiency Class-E Power Amplifiers for mmWave Radar Sensors: Design and Implementation

This article presents high-efficiency power amplifiers (PAs) implemented in Texas Instruments’ (TI) 130-nm BiCMOS process for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> - and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -band millimeter-wave (mmWave) radar sensors. A new Class-E output network based on a doubly tuned (DT) transformer is proposed to enable high-efficiency mmWave operation. The proposed Class-E network allows increasing PA device size beyond the traditional Class-E design limits while preserving nonoverlapping Class-E current and voltage waveforms. Design examples for single-ended and differential implementation of the proposed Class-E network are presented in this article. Three PA prototypes (a 79-GHz two-stage PA, a 63-GHz single-stage PA, and a 79-GHz single-stage PA) have been designed and fabricated in a high-volume production 130-nm BiCMOS process. A Class-E interstage network with a split-and-combine 45° transformer is employed in the two-stage PA to ease driving the last stage PA devices. Device layout optimization for improved efficiency is described in this article. The measurement results achieve a peak power-added efficiency (PAE) of 30.5%/34.7%/32.6% with an output power of 17/18.1/17 dBm for the 79-GHz two-stage, 63-GHz single-stage, and 79-GHz single-stage PAs, respectively. To the best of our knowledge, these are the record PAE numbers reported for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> - and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -band PAs in any silicon process, demonstrating the efficacy of the proposed DT transformer-based Class-E output network.

Systolic blood pressure measurement algorithm with mmWave radar sensor

Systolic blood pressure measurement algorithm with mmWave radar sensor

IMU2Doppler

The proliferation of sensors powered by state-of-the-art machine learning techniques can now infer context, recognize activities and enable interactions. A key component required to build these automated sensing systems is labeled training data. However, the cost of collecting and labeling new data impedes our ability to deploy new sensors to recognize human activities. We tackle this challenge using domain adaptation i.e., using existing labeled data in a different domain to aid the training of a machine learning model for a new sensor. In this paper, we use off-the-shelf smartwatch IMU datasets to train an activity recognition system for mmWave radar sensor with minimally labeled data. We demonstrate that despite the lack of extensive datasets for mmWave radar, we are able to use our domain adaptation approach to build an activity recognition system that classifies between 10 activities with an accuracy of 70% with only 15 seconds of labeled doppler data. We also present results for a range of available labeled data (10 - 30 seconds) and show that our approach outperforms the baseline in every single scenario. We take our approach a step further and show that multiple IMU datasets can be combined together to act as a single source for our domain adaptation approach. Lastly, we discuss the limitations of our work and how it can impact future research directions.

An mmWave Radar Based Real-Time Contactless Fitness Tracker Using Deep CNNs

“A healthy mind lives in a healthy body”. To maintain a healthy body, one has to follow a healthy lifestyle. Fitness trackers are excellent companions that help people meet their fitness goals and keep a healthy lifestyle. Thus, fitness tracking is becoming an integral part of the human lifestyle. But, most available fitness trackers nowadays require wearing/mounting on the body, which makes them unattractive for widespread use. To the contrary, mmwave radar sensors can accurately capture micro-motion parameters in a contactless configuration. In this paper, we propose and experimentally demonstrate the use of an mmwave radar sensor for real-time contactless fitness tracking using deep convolutional neural networks (CNNs). In this work, we created a setup to capture the body movements during various exercises by an mmwave radar. The radar data was processed and passed onto a deep CNN for training. Post-training results show that the system is able to classify different exercises in real-time with very high accuracy. The proposed system is computationally inexpensive and has the potential to become an effective alternative to body wearable fitness trackers for indoor fitness activities.

X-RIO: Radar Inertial Odometry with Multiple Radar Sensors and Yaw Aiding

A robust and accurate real-time navigation system is crucial for autonomous robotics. In particular, GNSS denied and poor visual conditions are still very challenging as vision based approaches tend to fail in darkness, direct sunlight, fog or smoke. Therefore, we are taking advantage of inertial data and FMCW radar sensors as both are not affected by such conditions. In this work, we propose a framework, which uses several 4D mmWave radar sensors simultaneously. The extrinsic calibration of each radar sensor is estimated online. Based on a single radar scan, the 3D ego velocity and optionally yaw measurements based on Manhattan world assumptions are fused. An extensive evaluation with real world datasets is presented. We achieve even better accuracies than state of the art stereo Visual Inertial Odometry (VIO) while being able to cope with degraded visual conditions and requiring only very little computational resources.

MmFall: Fall Detection Using 4-D mmWave Radar and a Hybrid Variational RNN AutoEncoder

Elderly fall prevention and detection becomes extremely crucial with the fast aging population globally. In this article, we propose <i>mmFall</i>, a novel fall detection system, which comprises 1) the emerging millimeter-wave (mmWave) radar sensor to collect the human body&#x2019;s point cloud along with the body centroid and 2) a hybrid variational recurrent neural network (RNN) autoencoder (HVRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is detected when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor offers privacy-compliance and high sensitivity to motion, over the traditional sensing modalities. However, 1) randomness in radar point cloud and 2) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two major challenges. To overcome the randomness in radar data, the proposed HVRAE uses variational inference, a generative approach rather than a discriminative approach, to infer the posterior probability of the body&#x2019;s latent motion state every frame, followed by a RNN to summarize the temporal features over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the HVRAE is built upon an autoencoder architecture in a semisupervised approach, which is only trained on the normal activities of daily living (ADL). In the inference stage, the HVRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment,<xref ref-type="fn" rid="fn1">¹</xref> we implemented the HVRAE along with two other baselines, and tested on the data set collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms baselines and achieves 98&#x0025; detection out of 50 falls at the expense of just 2 false alarms. <i>Note to Practitioners</i>&#x2014;Traditional nonwearable fall detection approaches typically make use of a vision-based sensor, such as camera, to monitor and detect fall using a classifier that is trained in a supervised fashion on the collected fall and nonfall data. However, several problems render these methods impractical. First, camera-based monitoring may trigger privacy concerns. Second, fall data collection using human subjects is difficult and costly, not to mention the impossible ask of the elderly repeating simulated falls for data collection. In this article, we propose a new fall detection approach to overcome these problems 1) using a palm-size mmWave radar sensor to monitor the elderly, that is highly sensitive to motion while protecting privacy and 2) using a semisupervised anomaly detection approach to circumvent the fall data collection. Further hardware engineering and more training data from people with different body figures could make the proposed fall detection solution even more practical.

Measurement and control method of clearance between wind turbine tower and blade-tip based on millimeter-wave radar sensor

This paper presented a method for measuring windturbine blade clearance based on a millimeter-wave radar sensor that operates at a frequency of 24 GHz. The values of the change in the distance between the tower and the blade during operation process were constantly measured through eight mm-wave radar sensors mounted on the surface of the tower. The measurement in the radar sensor installation coordinate system was converted to the value in the nacelle rotation coordinate system through coordinate transformation, and then the minimum clearance distance between the blade and the tower was estimated through the method of the least square curve regression fitting. Furthermore, a feedforward (FF) control method with variable blade angle was proposed to control the blade clearance distance. A case study was provided to prove its practicality. Experimental results from a 2.65 MW wind turbine prove that using the proposed control method, the blade clearance value increased from a minimum distance of 5.65 m before control to 7.43 m after control, an increase of 30%.

A 55–64-GHz Fully Integrated Miniaturized FMCW Radar Sensor Module for Short-Range Applications

A compact wide-bandwidth frequency-modulated continuous-wave (FMCW) radar sensor module with integrated antennas intended for operation in the unlicensed 60-GHz band is presented. The proposed FMCW transceiver chip is at the heart of the radar module and it incorporates a frequency synthesizer and the analog baseband circuitry. The mm-wave radar sensor achieves sub-2-cm spatial resolutions and enables cutting-edge modulation rates higher than 200 MHz/μs. Steep frequency slopes are particularly important in the detection of close proximity targets that are below 20-cm range. The FMCW radar sensor prototype chip is implemented in 0.13-μm SiGe BiCMOS technology. It delivers more than +17.5 dBm of effective isotropic radiated power at the output over the complete frequency range of interest while dissipating 690 mW. The module is packed into 32 mm × 22 mm × 7 mm aluminum case and covered by plastic lid.

Robust Gesture Recognition Using Millimetric-Wave Radar System

Gesture recognition is one of the most intuitive forms of humancomputer interface. Gesture sensing can replace interfaces such as touch and clicks needed for interacting with a device. In this article, we present a short-range compact 60-GHz mm-wave radar sensor that is sensitive to fine dynamic hand motions. A series of rangeDoppler images are extracted and processed using a long recurrent all-convolution neural network for real-time dynamic hand gesture recognition. Furthermore, we make use of novel data augmentation techniques for the proposed gesture recognition system to generalize for multiple users and operating environments. The results show accurate classification performance requiring very low processor footprint facilitating implementation in embedded platforms with real-time user feedback.

Gesture Recognition Using mm-Wave Sensor for Human-Car Interface

This article details the development of a gesture recognition technique using a mm-wave radar sensor for in-car infotainment control. Gesture recognition is becoming a more prominent form of human-computer interaction and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction. We use a 60 GHz mm-wave radar sensor to detect precise features of fine motion. Specific gesture features are extracted and used to build a machine learning engine that can perform real-time gesture recognition. This article discusses the user requirements and in-car environmental constraints that influenced design decisions. Accuracy results of the technique are presented, and recommendations for further research and improvements are made.