249 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
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar
Accurate motion capture of aerial robots in 3-D is a key enabler for
autonomous operation in indoor environments such as warehouses or factories, as
well as driving forward research in these areas. The most commonly used
solutions at present are optical motion capture (e.g. VICON) and Ultrawideband
(UWB), but these are costly and cumbersome to deploy, due to their requirement
of multiple cameras/sensors spaced around the tracking area. They also require
the drone to be modified to carry an active or passive marker. In this work, we
present an inexpensive system that can be rapidly installed, based on
single-chip millimeter wave (mmWave) radar. Importantly, the drone does not
need to be modified or equipped with any markers, as we exploit the Doppler
signals from the rotating propellers. Furthermore, 3-D tracking is possible
from a single point, greatly simplifying deployment. We develop a novel deep
neural network and demonstrate decimeter level 3-D tracking at 10Hz, achieving
better performance than classical baselines. Our hope is that this low-cost
system will act to catalyse inexpensive drone research and increased autonomy.Comment: Submitted to The 2021 International Conference on Robotics and
Automation (ICRA 2021
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
We survey the current state of millimeterwave (mmWave) radar applications in
robotics with a focus on unique capabilities, and discuss future opportunities
based on the state of the art. Frequency Modulated Continuous Wave (FMCW)
mmWave radars operating in the 76--81GHz range are an appealing alternative to
lidars, cameras and other sensors operating in the near visual spectrum. Radar
has been made more widely available in new packaging classes, more convenient
for robotics and its longer wavelengths have the ability to bypass visual
clutter such as fog, dust, and smoke. We begin by covering radar principles as
they relate to robotics. We then review the relevant new research across a
broad spectrum of robotics applications beginning with motion estimation,
localization, and mapping. We then cover object detection and classification,
and then close with an analysis of current datasets and calibration techniques
that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin
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
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
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