Mm-wave FMCW Research Articles

An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting

An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting

A Study on Adaptively Adjusting Phases Accumulative Number for Remote Heart-Rate Sensing with mm-wave Multi-Chirp FMCW Radar

A Study on Adaptively Adjusting Phases Accumulative Number for Remote Heart-Rate Sensing with mm-wave Multi-Chirp FMCW Radar

A Novel Remote-Tracking Heart Rate Measurement Method Based on Stepping Motor and mm-Wave FMCW Radar

A Novel Remote-Tracking Heart Rate Measurement Method Based on Stepping Motor and mm-Wave FMCW Radar

A Study of Vital Signs Monitoring Mobility Robot Based on mm-Wave FMCW Radar

A Study of Vital Signs Monitoring Mobility Robot Based on mm-Wave FMCW Radar

Displacement Evaluation by mmWave FMCW Radars: Method and Performance Metrics

Displacement Evaluation by mmWave FMCW Radars: Method and Performance Metrics

Scalable Multi-Subject Vital Sign Monitoring with mmWave FMCW Radar and FPGA Prototyping

Scalable Multi-Subject Vital Sign Monitoring with mmWave FMCW Radar and FPGA Prototyping

Towards Unconstrained Vocabulary Eavesdropping With mmWave Radar Using GAN

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this paper, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">milliEar</small> , a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">milliEar</small> combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">milliEar</small> using off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">milliEar</small> can accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

Radar-Beat: Contactless beat-by-beat heart rate monitoring for life scenes

Background and objective:Heartbeat is a crucial vital sign, and radio-frequency technology can measure the micro-vibration of the body surface from a distance and extract the heartbeat signal, which can solve the compliance problem caused by wearable sensors. However, most existing works verify short-term controlled experiments and seldom consider the challenges posed by random body movements and posture changes during long-term monitoring in daily life scenarios. In this article, we propose a systematic solution called Radar-Beat, which enables accurate heartbeat monitoring using an mmWave FMCW radar device. Our goal is to promote the integration of contactless heartbeat monitoring into life scenarios. Methods:We proposes a sensitive body motion detection algorithm and an optimal Range-bin selection algorithm, which can automatically identify body motion and update the best heartbeat signal channel. Then, we construct a personalized heartbeat template for each signal segment and propose a global optimization model to improve the accuracy of heartbeat length estimation. Results:We use the synchronized ECG signal as the ground truth, results show strong agreement between Radar-Beat and synchrony ECG devices for heart rate and inter-beat interval (IBI) measurements in healthy subjects. In the heartbeat monitoring experiment for a total of 72 h and 56 min involving 11 participants, under a time coverage of 91.2%, the median error of IBI estimation was 12 ms. Conclusion:Radar-Beat has strong robustness to different individuals in different postures and positions. It can be deployed in a hospital bed or home to enable continuous heartbeat monitoring in an unobtrusive way.

CubeLearn: End-to-End Learning for Human Motion Recognition From Raw mmWave Radar Signals

mmWave FMCW radar has attracted huge amount of research interest for human-centered applications in recent years, such as human gesture and activity recognition. Most existing pipelines are built upon conventional Discrete Fourier Transform (DFT) pre-processing and deep neural network classifier hybrid methods, with a majority of previous works focusing on designing the downstream classifier to improve overall accuracy. In this work, we take a step back and look at the pre-processing module. To avoid the drawbacks of conventional DFT pre-processing, we propose a complex-weighted learnable pre-processing module, named CubeLearn, to directly extract features from raw radar signal and build an end-to-end deep neural network for mmWave FMCW radar motion recognition applications. Extensive experiments show that our CubeLearn module consistently improves the classification accuracies of different pipelines, especially benefiting those simpler models, which are more likely to be used on edge devices due to their computational efficiency. We provide ablation studies on initialization methods and structure of the proposed module, as well as an evaluation of the running time on PC and edge devices. This work also serves as a comparison of different approaches towards data cube slicing. Through our task agnostic design, we propose a first step towards a generic end-to-end solution for radar recognition problems.

AI-Powered Noncontact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing

In this work, we present a cloud-based system for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a dataset of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

Short-Time Remote Heart Rate Measurement Based on mmWave FMCW Radar Frame Structure

This paper presents a novel method to reduce measurement time for remote heart rate measurement using mmWave FMCW radar. The proposed method is based on the sampling theory and the frame structure of recent FMCW radar systems. The range displacement from heartbeat is measured by 60GHz FMCW radar with a fixed frame interval, 50ms. With additional chirp repetition in each frame, the heartbeat signal can be sampled with increased or decreased interval. By adopting three different sampling rates from a single radar measurement, the resolution of heart rate measurement is improved in a short time, 2.5s. The proposed method is evaluated with a commercial off-the-shelf FMCW radar system and three different human subject, for no respiration and natural status. To evaluate the performance of the proposed method, the heart rate result is compared with ECG sensor and conventional signal processing method. The results show that the proposed method estimates the heart rate with lower error and shorter measurement time.

A Novel Scheme of High-Precision Heart Rate Detection With a mm-Wave FMCW Radar

A Novel Scheme of High-Precision Heart Rate Detection With a mm-Wave FMCW Radar

Multi-Feature Transformer-Based Learning for Continuous Human Motion Recognition with High Similarity Using mmWave FMCW Radar.

Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes.

The Effect of Multi-Directional on Remote Heart Rate Measurement Using PA-LI Joint ICEEMDAN Method with mm-Wave FMCW Radar

The Effect of Multi-Directional on Remote Heart Rate Measurement Using PA-LI Joint ICEEMDAN Method with mm-Wave FMCW Radar

Localization of Multi-Class On-Road and Aerial Targets Using mmWave FMCW Radar

mmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are carried out in a variety of practical scenarios including pedestrians, a car, and an UAV, also known as a drone. With measured data, range-angle maps are created, and morphological operators are used to estimate the AoA of the targets. We also process radar range-angle images for improved visual representation. The proposed method will be extremely beneficial for practical ground stations, traffic control and monitoring frameworks for both on-ground and airborne vehicles.

MIMO-SAR: A Hierarchical High-Resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform.

A novel adaptive range-bin selection method for remote heart-rate measurement of an indoor moving person using mm-wave FMCW radar

A novel adaptive range-bin selection method for remote heart-rate measurement of an indoor moving person using mm-wave FMCW radar

A Novel Angle Estimation for mmWave FMCW Radars Using Machine Learning

In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 - 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.56 degrees is achieved. These proposed techniques will be highly useful for several applications in cost-effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

Remote heart-rate estimation based on phase accumulation-linear interpolation method for mm-wave FMCW radar

Remote heart-rate estimation based on phase accumulation-linear interpolation method for mm-wave FMCW radar

Millimeter wave radar data of people walking.

This dataset contains complex signals coming from a mmWave FMCW radar system. Signals were acquired during a measurement campaign taken indoor and aimed to assess people's different ways of walking. Measurement setup and devices are described.The dataset consists of the acquisitions of six different types of activities, performed by 29 subjects who repeat each activity several times. Therefore, the dataset contains multiple different experiments for each activity, for a total of 231 acquisitions. The subjects walk without any constraint or do not follow any pattern, thus making this dataset useful not only for human gait recognition but also to evaluate the performance of different radar data processing algorithms.