688 research outputs found
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.publishedVersio
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
In modern on-driving computing environments, many sensors are used for
context-aware applications. This paper utilizes two deep learning models, U-Net
and EfficientNet, which consist of a convolutional neural network (CNN), to
detect hand gestures and remove noise in the Range Doppler Map image that was
measured through a millimeter-wave (mmWave) radar. To improve the performance
of classification, accurate pre-processing algorithms are essential. Therefore,
a novel pre-processing approach to denoise images before entering the first
deep learning model stage increases the accuracy of classification. Thus, this
paper proposes a deep neural network based high-performance nonlinear
pre-processing method.Comment: 4 pages, 7 figure
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
Human gesture recognition using millimeter wave (mmWave) signals provides
attractive applications including smart home and in-car interface. While
existing works achieve promising performance under controlled settings,
practical applications are still limited due to the need of intensive data
collection, extra training efforts when adapting to new domains (i.e.
environments, persons and locations) and poor performance for real-time
recognition. In this paper, we propose DI-Gesture, a domain-independent and
real-time mmWave gesture recognition system. Specifically, we first derive the
signal variation corresponding to human gestures with spatial-temporal
processing. To enhance the robustness of the system and reduce data collecting
efforts, we design a data augmentation framework based on the correlation
between signal patterns and gesture variations. Furthermore, we propose a
dynamic window mechanism to perform gesture segmentation automatically and
accurately, thus enable real-time recognition. Finally, we build a lightweight
neural network to extract spatial-temporal information from the data for
gesture classification. Extensive experimental results show DI-Gesture achieves
an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments
and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre
reaches over 97% with average inference time of 2.87ms, which demonstrates the
superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile
Computing. And it is still under revie
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
In this paper, a real-time signal processing frame-work based on a 60 GHz
frequency-modulated continuous wave (FMCW) radar system to recognize gestures
is proposed. In order to improve the robustness of the radar-based gesture
recognition system, the proposed framework extracts a comprehensive hand
profile, including range, Doppler, azimuth and elevation, over multiple
measurement-cycles and encodes them into a feature cube. Rather than feeding
the range-Doppler spectrum sequence into a deep convolutional neural network
(CNN) connected with recurrent neural networks, the proposed framework takes
the aforementioned feature cube as input of a shallow CNN for gesture
recognition to reduce the computational complexity. In addition, we develop a
hand activity detection (HAD) algorithm to automatize the detection of gestures
in real-time case. The proposed HAD can capture the time-stamp at which a
gesture finishes and feeds the hand profile of all the relevant
measurement-cycles before this time-stamp into the CNN with low latency. Since
the proposed framework is able to detect and classify gestures at limited
computational cost, it could be deployed in an edge-computing platform for
real-time applications, whose performance is notedly inferior to a
state-of-the-art personal computer. The experimental results show that the
proposed framework has the capability of classifying 12 gestures in real-time
with a high F1-score.Comment: Accepted for publication in IEEE Sensors Journal. A video is
available on https://youtu.be/IR5NnZvZBL
Multi-User Gesture Recognition with Radar Technology
The aim of this work is the development of a Radar system for consumer applications. It is capable of tracking multiple people in a room and offers a touchless human-machine interface for purposes that range from entertainment to hygiene
- …