Article
Engineering, Electrical & Electronic
Nicolas Rasmont, Hussein Al-Rashdan, Gregory Elliott, Joshua Rovey, Laura Villafane
Summary: This paper presents a novel method for measuring the concentration of particles in optically opaque particle-laden flows. The method utilizes millimeter wave interferometry to measure the path-integrated particle concentrations using a fully-integrated FMCW radar. The instrument is capable of high-speed and quantitative measurements in dispersed multiphase flows with higher concentrations than existing optical methods. Calibration and validation experiments demonstrate the effectiveness of the measurement concept.
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES
(2023)
Article
Environmental Sciences
Yuming Zeng, Siyi Shen, Zhiwei Xu
Summary: Feature extraction and recognition of underwater targets were studied using a millimeter wave (mmWave) radar. The continuous wavelet transform (CWT) method showed better detection performance, and the mmWave radar accurately detected even weak water surface acoustic wave (WSAW) signals. The experiments demonstrated the potential of mmWave radar for cross-medium detection and recognition of underwater targets.
Article
Engineering, Electrical & Electronic
Eric Klinefelter, Jason Merlo, Hayder Radha, Jeffrey A. Nanzer
Summary: In this work, the exact response of an interferometric correlation radar to a scene with multiple independent point scatterers is derived. An accurate signal model is crucial for predicting and mitigating intermodulation responses generated by nonlinear processing. Both the exact and approximate models are validated through simulation and experimental measurement. The feasibility of these models is demonstrated by showing their accurate representation of the measured or simulated interferometric response.
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES
(2022)
Article
Computer Science, Theory & Methods
Yanlong Qiu, Jiaxi Zhang, Yanjiao Chen, Jin Zhang, Bo Ji
Summary: This paper proposes a practical system called Radar(2) for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. It can detect and localize spy radars with high accuracy and detection rate.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Chemistry, Multidisciplinary
Yael Balal, Afik Yarimi, Nezah Balal
Summary: Falls are the leading cause of accidents among the elderly population. In recent years, radar has been widely employed in fall detection, providing superior sensing capabilities, small dimensions, low cost, non-intrusive sensing, and robustness. This paper presents a technique using low-power millimeter-wave radar to identify when a person is falling in real time, based on micro-Doppler shifts associated with the person's motion. Experimental results show high-resolution and accurate results using 94 GHz real radar data.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Samuel Wagner, Ahmad Alkasimi, Anh-Vu Pham
Summary: This research employs a millimeter-wave radar to detect anomalous vibrations during highway-speed vehicle transport, specifically to detect drilling activities on the sidewalls of secure metallic containers. The study presents a signal-processing pipeline and a statistical model to enhance the detection capabilities of the radar sensor.
Article
Engineering, Electrical & Electronic
Jinlei Hou, Gao Chen, Qingfeng Zhou, Chanzi Liu, Xiangling Zuo, Yajuan Tang, Chi-Tsun Cheng
Summary: This paper proposes an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit to address the problem of detecting humans in indoor environments in the presence of moving clutter sources. Unlike existing methods, which rely on both distance and micro-Doppler information, our method only relies on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Through time-frequency analyses on radar data, seven features are extracted from spectrograms and fed into a random forest classifier to categorize the state of a room into five scenarios, achieving an accuracy of 97.5%.
PHYSICAL COMMUNICATION
(2023)
Article
Engineering, Electrical & Electronic
Eric Klinefelter, Jeffrey A. Nanzer
Summary: This article demonstrates the use of interferometric radar for high-accuracy measurement of ground speed of moving vehicles, showing the potential application of this technology in automotive velocity estimation. Through mathematical modeling and two velocity estimation methods, the authors present the feasibility of the new interferometric radar system in the automotive field.
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES
(2021)
Article
Geochemistry & Geophysics
Fei Qin, Xiangxi Bu, Zhiyuan Zeng, Xiangwei Dang, Xingdong Liang
Summary: Foreign object debris (FOD) radar systems are widely used to detect foreign materials that pose a threat to aircraft and personnel. This study introduces a millimeter-wave radar detection approach based on compressed sensing and compressed imaging, which effectively detects small targets by suppressing sidelobes and improving resolution. Experimental results demonstrate improved efficiency in scenarios with small intervals between multiple targets and strong scattering coverage of small targets. Additionally, this study highlights the previously unrealized benefits of applying compressed imaging to enhance the performance of FOD radar systems.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Bin Tan, Zhixiong Ma, Xichan Zhu, Sen Li, Lianqing Zheng, Sihan Chen, Libo Huang, Jie Bai
Summary: This study proposes a 3-D object-detection framework based on a multiframe 4-D millimeter-wave radar point cloud. By matching between millimeter-wave radar frames, the relative velocity information of the point cloud is compensated for the absolute velocity. The proposed framework outperformed the comparison methods based on the 3-D mean average precision.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Ariyamehr Rezaei, Alessandro Mascheroni, Michael C. Stevens, Reza Argha, Michela Papandrea, Alessandro Puiatti, Nigel H. Lovell
Summary: As the population ages, wearable devices offer a solution for unobtrusively detecting falls. The millimeter-wave radar technology was used in this study to collect data from healthy young volunteers. Different classifiers, including multilayer perceptron, random forest, k-nearest neighbor, and support vector machine, were applied to the extracted features. Additionally, a convolutional neural network based on deep learning was proposed. Results showed that the random forest classifier achieved the best accuracy, and the CNN model performed slightly better, suggesting the feasibility of using mmWave radar for unobtrusive fall detection.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Zhaolin Zhang, Wugang Meng, Mingqi Song, Yuhan Liu, Yinan Zhao, Xiang Feng, Fengcong Li
Summary: This paper proposes a recognition method based on multi-angle radar observation for human behavior recognition. The energy domain ratio method is used to select a radar with more sensitive features, and local tangent space alignment and adaptive ELM are applied to improve the recognition rate in a high-noise environment. A multi-angle entropy feature and an improved ELM are developed to identify human micro-motion in a low-noise indoor environment, and the effect of observation distance on the recognition effect is explored.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Environmental Sciences
Zhihuo Xu, Shuaikang Xue, Yuexia Wang
Summary: This article presents a sparsity-based technique to mitigate the incoherent interference between automotive radars. It uses a low-pass filter to detect the interference envelope and restores the radar echoes using L1 norm-regularized least squares. The alternating direction method of multipliers is then applied to restore the radar echoes.
Article
Environmental Sciences
Xindi Liu, Yunhua Wang, Fushun Liu, Yuting Zhang
Summary: The feasibility of using millimeter-wave radars for wave observations was investigated in this study. Through observations in laboratory and field environments, one-dimensional and two-dimensional wave spectra were extracted, and important wave parameters were calculated. The results indicate that millimeter-wave radars are capable of observing water waves, with measurement results consistent with existing wave observation devices, and the wave direction measurements closely aligned with the actual wave directions.
Article
Environmental Sciences
Fengde Jia, Jihong Tan, Xiaochen Lu, Junhui Qian
Summary: With the development of autonomous driving and intelligent traffic scenarios, deep learning-based object detection technology is widely applied to real traffic scenarios. LiDAR and cameras are commonly used detection devices, but they are sensitive to light and can be affected by night and bad weather conditions. However, millimeter-wave radar can overcome these challenges and has a great auxiliary effect on safe driving.
