UWB Radar Research Articles

Tracing Human Stress From Physiological Signals Using UWB Radar

Tracing Human Stress From Physiological Signals Using UWB Radar

Extraction of Vital Sign Based on PE and ICEEMDAN for UWB Radar

Extraction of Vital Sign Based on PE and ICEEMDAN for UWB Radar

Non-Line-of-Sight Vital Sign Detection Using Multipath Propagation of UWB Radar

Non-Line-of-Sight Vital Sign Detection Using Multipath Propagation of UWB Radar

MDST: 2-D Human Pose Estimation for SISO UWB Radar Based on Micro-Doppler Signature via Cascade and Parallel Swin Transformer

MDST: 2-D Human Pose Estimation for SISO UWB Radar Based on Micro-Doppler Signature via Cascade and Parallel Swin Transformer

UWB Radar Imaging Method for the Longwall Retreat and Creep Displacements Measurement in Underground Coal Mine

UWB Radar Imaging Method for the Longwall Retreat and Creep Displacements Measurement in Underground Coal Mine

Non-invasive vibration analysis with RDWT, UWB radar, and software PLL for open-phase and multiple bearing faults diagnosis in BLDC motor

Non-invasive vibration analysis with RDWT, UWB radar, and software PLL for open-phase and multiple bearing faults diagnosis in BLDC motor

UHead

The focus of Advanced driver-assistance systems (ADAS) is extending from the vehicle and road conditions to the driver because the driver's attention is critical to driving safety. Although existing sensor and camera based methods can monitor driver attention, they rely on specialised hardware and environmental conditions. In this paper, we aim to develop an effective and easy-to-use driver attention monitoring system based on UWB radar. We exploit the strong association between head motions and driver attention and propose UHead that infers driver attention by monitoring the direction and angle of the driver's head rotation. The core idea is to extract rotational time-frequency representation from reflected signals and to estimate head rotation angles from complex head reflections. To eliminate the dynamic noise generated by other body parts, UHead leverages the large magnitude and high velocity of head rotation to extract head motion information from the dynamically coupled information. UHead uses a bilinear joint time-frequency representation to avoid the loss of time and frequency resolution caused by windowing of traditional methods. We also design a head structure-based rotation angle estimation algorithm to accurately estimate the rotation angle from the time-varying rotation information of multiple reflection points in the head. Experimental results show that we achieve 12.96° median error of 3D head rotation angle estimation in real vehicle scenes.

A public dataset of dogs vital signs recorded with ultra wideband radar and reference sensors

Recently, radar sensors have been extensively used for vital sign monitoring in dogs, owing to their noncontact and noninvasive nature. However, a public dataset on dog vital signs has yet to be proposed since capturing data from dogs requires special training and approval. This work presents the first ever ultra wideband radar-based dog vital sign (UWB-DVS) dataset, which was captured in two independent scenarios. In the first scenario, clinical reference sensors are attached to the fainted dogs, and data from UWB radar and reference sensors are captured synchronously. In the second scenario, the dogs can move freely, and video recordings are provided as a reference for movement detection and breathing extraction. For technical validation, a high correlation, above 0.9, is found between the radar and clinical reference sensors for both the heart rate and breathing rate measurements in scenario 1. In scenario 2, the vital signs and movement of the dogs are shown in the form of dashboards, demonstrating the long-term monitoring capability of the radar sensor.

A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions

A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions

Lightweight Self-Supervised Recognition of Small-Sample Ships Using Micro-Doppler Signatures and UAV-Based UWB Radar

Lightweight Self-Supervised Recognition of Small-Sample Ships Using Micro-Doppler Signatures and UAV-Based UWB Radar

VibWall : Smartphone’s Vibration Challenge-response for Wall Crack Detection

As the building ages, the wall structure may become deteriorated (e.g., wall cracks, discontinuities, and corrosion) due to the variation of the environment (i.e., temperature and humidity). Moreover, these wall cracks, discontinuities, and corrosion will affect the living comfort and coziness. As such, the wall health diagnostic becomes crucial for the safety and comfort of modern buildings. However, the existing wall health detection techniques (e.g., UWB radars, acoustic sensing, and sensor embedding techniques) are high-cost, not ubiquitous, and not robust to the variation of the environment. In this article, we propose VibWall , a system that can use the smartphone’s sensors (i.e., accelerometer, gyroscope, and vibrator) to detect the wall’s structural health. Specifically, the wall cracks can be detected for living safety, comfort, and coziness. Our key idea is that the smartphone’s vibration is absorbed, reflected, and propagated disparately based on the physical structure of the wall. To be specific, we employ a novel challenge-response scheme, where the challenge is a sequence of heterogeneous vibration patterns from the smartphone’s vibrator, and the responses to these vibrations are sensed by the smartphone’s gyroscope and accelerometer sensors. Then, the machine learning-based classifier (e.g., random forest classifier) will be used to discriminate between the healthy wall and the wall with cracks, discontinuities, or corrosion based on these responses. Our experimental results show good performance on the wall’s structural health detection with the wall specimen and real-world walls.

New Paradigm for Contactless Vital Sign Sensing Using UWB Radar and Hybrid Optical Wireless Communications

New Paradigm for Contactless Vital Sign Sensing Using UWB Radar and Hybrid Optical Wireless Communications

UWB Radar SLAM: An Anchorless Approach in Vision Denied Indoor Environments

UWB Radar SLAM: An Anchorless Approach in Vision Denied Indoor Environments

Digit Recognition Using FMCW and UWB Radar Sensors: A Transfer Learning Approach

Digit Recognition Using FMCW and UWB Radar Sensors: A Transfer Learning Approach

Dilemmas and directions of real-time detecting trapped individuals using ultra-wideband radar in building fire rescue

Abstract The crucial component of a successful building fire rescue is detecting trapped individuals efficiently and supporting firefighters in planning rescue and evacuation routes simultaneously. Due to its insensitivity to heat flow, great penetration, and high accuracy, Ultra-Wideband radar is widely used in autonomous driving and medical monitoring, but it has not yet been shown its full potential in fire rescue. In light of this, a summary of eight technical challenges for finding and rescuing trapped individuals in typical building fire scenarios is provided, which could be the traction to investigate the capability state of UWB radar technology. Five technological issues that restrict the use of UWB radar for fire rescue scene detection are discussed followed. Finally, 10 important technologies are presented in three directions to deal with the issues at hand, and several advanced technologies are planned in two directions to improve the performance of UWB radar in fire rescue.

Fill level measurement of low-permittivity material using an M-sequence UWB radar

AbstractDue to increasingly complex and automated manufacturing processes, the demands on the control parameters of these processes are also increasing. One parameter is the fill quantity of, e.g., liquids in production plants, whose precise determination is of ever-growing importance. Up to now, the exact level of determination under difficult conditions, such as high ambient temperatures, has been a particular challenge. This paper demonstrates a novel method by which an M-sequence UWB radar can determine levels of low-permittivity materials in small metal containers. For this purpose, hot melt is used as an example. Thus, the influence of large temperature differences on the long-term stability of level measurement can also be investigated. The measurements show that the level of hot melt can be measured to be long-term stable with an accuracy of better than 3 mm. Furthermore, the precise determination of the empty state is highly important for many applications. For this reason, this paper shows a method for determining the empty state without complex calibration procedures. For the empty level indication, an accuracy of up to 0.5 mm could be achieved for molten hot glue and 3% of the tank volume, independent of the shape or aggregate state of the medium.

Data filtering and deep learning for enhanced human activity recognition from UWB radars

Data filtering and deep learning for enhanced human activity recognition from UWB radars

A 0.9 - 1.5 GHz CMOS UWB Radar IC for Through the Wall Human Detection

In this paper, a UWB radar IC for the through wall radar is presented to achieve the smaller form factor of the module and low power operation. For the penetration performance enhancement, the radar IC s designed for the lower 0.9 – 1.5 GHz band. The radar IC employs the 4-channel time-interleaved equivalent time sampling receiver for 200 ps fine resolution, and the digitally synthesized impulse generator for the transmitter. The human target behind the concrete brick wall can be detected in the distance over 9.4 m with the help of signal processing part and additional amplifiers. The radar transceiver IC is fabricated in a 0.13 μm CMOS technology and the current consumption is 116.3 mA at the 1.2 V supply.

UWB Radar Applied to Lane Occupation and Vehicle Classification

This article describes the use of a commercial UWB radar for vehicle classification and lane occupation detection using real-world data acquired in an urban environment. We compare two radar image processing schemes: one based on deep learning using raw data produced by the radar, and a second method employing traditional machine learning algorithms using features extracted from raw data. We verify experimentally that both schemes lead to reasonably accurate estimates without the need of large training sets.

Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar

Gesture-ProxylessNAS: A Lightweight Network for Mid-Air Gesture Recognition Based on UWB Radar