Frequency Modulated Continuous Wave Radar Research Articles

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

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%.

Augmented Reality Terahertz (AR-THz) Sensing and Imaging with Frequency-Modulated Continuous-Wave Radar

Augmented Reality Terahertz (AR-THz) Sensing and Imaging with Frequency-Modulated Continuous-Wave Radar

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave (FMCW) radar is an advantageous sensor scheme for target estimation and environmental perception. However, existing algorithms based on discrete Fourier transform (DFT), multiple signal classification (MUSIC) and compressed sensing, etc., cannot achieve both low complexity and high resolution simultaneously. This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar. Firstly, we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy. Secondly, we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data, which improves the efficiency of continuous target tracking. These two approaches have improved the algorithm efficiency by nearly 2–4 orders of magnitude without losing accuracy and resolution. Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Fully Integrated 24-GHz 1TX-2RX Transceiver for Compact FMCW Radar Applications.

A fully integrated 24-GHz radar transceiver with one transmitter (TX) and two receivers (RXs) for compact frequency modulated continuous wave (FMCW) radar applications is here presented. The FMCW synthesizer was realized using a fractional-N phase-locked loop (PLL) and programmable chirp generator, which are completely integrated in the proposed transceiver. The measured output phase noise of the synthesizer is -80 dBc/Hz at 100 kHz offset. The TX consists of a three-bit bridged t-type attenuator for gain control, a two-stage drive amplifier (DA) and a one-stage power amplifier (PA). The TX chain provides an output power of 13 dBm while achieving <0.5 dB output power variation within the range of 24 to 24.25 GHz. The RX with a direct conversion I-Q structure is composed of a two-stage low noise amplifier (LNA), I-Q generator, mixer, transimpedance amplifier (TIA), a two-stage biquad band pass filter (BPF), and a differential-to-single (DTS) amplifier. The TIA and the BPF employ a DC offset cancellation (DCOC) circuit to suppress the strong reflection signal and TX-RX leakage. The RX chain exhibits an overall gain of 100 dB. The proposed radar transceiver is fabricated using a 65 nm CMOS technology. The transceiver consumes 220 mW from a 1 V supply voltage and has 4.84 mm2 die size including all pads. The prototype FMCW radar is realized with the proposed transceiver and Yagi antenna to verify the radar functionality, such as the distance and angle of targets.

A Clutter Identification and Removal Method Based on Long Delay Lines and Cross-Correlation in Through-Wall Detection

Life detection is important in earthquake rescue, but weak vital signal is susceptible to interference by clutters. Due to the undesirable characteristics of the hardware, there are two main types of clutter generated by the frequency modulation continuous wave (FMCW) radar when transmitting signals. One is generated by periodic nonlinearity during frequency modulation, and the other is generated by the phase-locked loop spuriousness (PLLS) in frequency division. They cause additional beat frequencies to appear beside those from the target, leading to false alarms. Since the suppression measures for them are different, it is necessary to distinguish the types of clutter and choose appropriate suppression methods. In this paper, the accurate theoretical modeling of the effects of the periodic nonlinearity and phase-locked loop spuriousness on the beat signal is performed to determine distinctions between them. The clutter occurring in the system used is identified as originating from phase-locked loop spuriousness through fiber-optic experiments. A method using long delay lines and cross-correlation is proposed to identify and remove it. In experiments, the false alarm rate is reduced from over 50 percent to nearly 0 percent, providing strong evidence for the effectiveness of the proposed method in through-wall detection.

Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network With Attention.

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Fall Detection Using FMCW Radar to Reduce Detection Errors for the Elderly

Fall accidents pose a significant threat of severe injuries for the elderly, who often need immediate assistance when they fall. Since the use of conventional contact sensors or cameras might be uncomfortable for the user, research on fall detection using non-contact sensors has received considerable attention. While most prior studies have relied heavily on Doppler-based velocity parameters to detect falls, using only Doppler information may lead to erroneous detection of fall-like behavior. As a result, a feature that accounts for additional information is necessary. Addressing this need, this study developed an algorithm for classifying falls by detecting human motions using frequency modulation continuous wave radar, proposing a novel feature to reduce detection errors. The suggested feature was computed using the rangevelocity map of the 2D Fourier transform and evaluated using supervised machine learning techniques, such as support vector machine and linear discriminant analysis, attaining an accuracy higher than 91%.

Classification of Small Drones Using Low-Uncertainty Micro-Doppler Signature Images and Ultra-Lightweight Convolutional Neural Network.

Many studies have attempted to classify small drones in response to threats posed by the technical progress of small drones. Recently, small drones have been classified utilizing convolutional neural networks (CNNs) with micro-Doppler signature (MDS) images generated from frequency-modulated continuous-wave (FMCW) radars. This study proposes a comprehensive method for classifying small drones in real-time using high-quality MDS images and an ultra-lightweight CNN. The proposed comprehensive method comprises an MDS image generation technique, which can improve the quality of MDS images generated via FMCW radars, and the ultra-lightweight CNN with high accuracy performance despite its remarkable lightness. Experimental results show that the proposed MDS image generation technique increases the accuracy of CNNs by enhancing the MDS image quality. This is further verified using the results of uncertainty quantification. The proposed ultra-lightweight CNN significantly decreases the computational cost while achieving high accuracy. Finally, we demonstrate that the proposed comprehensive method successfully classifies small drones from far distances with high efficiency and accuracy: the maximum and average accuracies for classification are 100% and 99.21%, respectively, and the numbers of parameters, nodes, and floating-point operations of the proposed ultra-lightweight CNN are approximately 4.88 K, 21.51 K, and 31.52 M, respectively.

A 0.46–0.52-THz fully-differential quasi-monostatic FMCW radar transceiver in 90-nm SiGe BiCMOS

A 0.46–0.52-THz fully-differential quasi-monostatic frequency-modulated continuous-wave (FMCW) radar transceiver was designed in a 90-nm SiGe bipolar CMOS technology with fT/fmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extit{f}_{ \ extit{T}}/{f}_{\ ext{max}}$$\\end{document} of 300/480 GHz. The transceiver chip has an area of 2.84 mm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{mm}^{2}$$\\end{document}. A multiply-by-24 frequency multiplier chain is used to generate a 0.48-THz signal from a 20-GHz input signal. Two 0.48-THz push–push doublers are driven in quadrature for a differential output signal with a measured effective isotropic radiated power of up to -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}8.8 dBm at the transmitter at 0.47 THz. A differential subharmonic receiver mixer with a stacked quad topology achieves a measured system conversion gain of 7 dB and a measured single-sideband noise figure of 25.2 dB at 0.471 THz. FMCW radar measurements are performed using an on-board frequency source of a credit card-sized demonstrator at a bandwidth of 60 GHz.

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application. The code and dataset will be made publicly available. The code and dataset are available here 11https://github.com/FrusqueGaetan/Focus_SVDD_EAD.

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

This paper introduces a computationally inexpensive technique for moving target detection in challenging outdoor environments using millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radars leveraging traditional signal processing methodologies. Conventional learning-based techniques for moving target detection suffer when there are variations in environmental conditions. Hence, the work described here leverages robust digital signal processing (DSP) methods, including wavelet transform, FIR filtering, and peak detection, to efficiently address variations in reflective data. The evaluation of this method is conducted in an outdoor environment, which includes obstructions like woods and trees, producing an accuracy score of 92.0% and precision of 91.5%. Notably, this approach outperforms deep learning methods when it comes to operating in changing environments that project extreme data variations.

Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision

Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and velocity measurements, while vision sensors capture fine-grained visual information. By integrating data from these modalities, the system achieves a comprehensive understanding of hand gestures, resulting in improved gesture recognition accuracy and robustness. The proposed system comprises three main stages: data acquisition, gesture recognition, and multimodal fusion. In the data acquisition stage, synchronized data streams from FMCW radar and vision sensors are captured. Then, machine learning algorithms are employed in the gesture recognition stage to classify and interpret hand gestures. Finally, the multimodal fusion stage aligns and fuses the data, creating a unified representation that captures the spatial and temporal aspects of hand gestures, enabling real-time control commands for the UAV. Experimental results demonstrate the system‘s effectiveness in accurately recognizing and responding to hand gestures. The multimodal fusion of FMCW radar and vision sensors enables a robust and versatile gesture-based control interface.

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%.

Grouped People Counting Using mm-Wave FMCW MIMO Radar

The problem of radar-based counting of multiple individuals moving as a single group is addressed using a mm-wave multiple-input-multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. This problem is challenging because the different individuals are closer to each other than the range/azimuth resolution, and their bulk Doppler signatures are difficult to distinguish, as they tend to move together. A processing pipeline is proposed, based on the combination of a multiple target tracking algorithm with a classifier to track each group and count the number of people within. Specific salient features are defined for the classifier and extracted from range-azimuth maps and cadence velocity diagrams (CVD). The proposed pipeline has been experimentally validated in several outdoor scenarios with grouped people. The results show that the combination of tracking algorithm and classifier in the proposed pipeline outperforms alternative methods from the literature as well as a commercial toolbox for people counting.

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.