981 research outputs found
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
The Feasibility of Quantitatively Characterizing the Vehicle Motion Environment (VME)
https://deepblue.lib.umich.edu/bitstream/2027.42/154108/1/ervin1990.pd
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
Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
Integrating efficient connectivity, positioning and sensing functionalities
into 5G New Radio (NR) and beyond mobile cellular systems is one timely
research paradigm, especially at mm-wave and sub-THz bands. In this article, we
address the radio-based sensing and environment mapping prospect with specific
emphasis on the user equipment (UE) side. We first describe an efficient
l1-regularized least-squares (LS) approach to obtain sparse range--angle charts
at individual measurement or sensing locations. For the subsequent environment
mapping, we then introduce a novel state model for mapping diffuse and specular
scattering, which allows efficient tracking of individual scatterers over time
using interacting multiple model (IMM) extended Kalman filter and smoother. We
provide extensive numerical indoor mapping results at the 28~GHz band deploying
OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both
accurate ray-tracing based as well as actual RF measurement results. The
results illustrate the superiority of the dynamic tracking-based solutions,
compared to static reference methods, while overall demonstrate the excellent
prospects of radio-based mobile environment sensing and mapping in future
mm-wave networks
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
Multi-Base Station Cooperative Sensing with AI-Aided Tracking
In this work, we investigate the performance of a joint sensing and
communication (JSC) network consisting of multiple base stations (BSs) that
cooperate through a fusion center (FC) to exchange information about the sensed
environment while concurrently establishing communication links with a set of
user equipments (UEs). Each BS within the network operates as a monostatic
radar system, enabling comprehensive scanning of the monitored area and
generating range-angle maps that provide information regarding the position of
a group of heterogeneous objects. The acquired maps are subsequently fused in
the FC. Then, a convolutional neural network (CNN) is employed to infer the
category of the targets, e.g., pedestrians or vehicles, and such information is
exploited by an adaptive clustering algorithm to group the detections
originating from the same target more effectively. Finally, two multi-target
tracking algorithms, the probability hypothesis density (PHD) filter and
multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the
targets. Numerical results demonstrated that our framework could provide
remarkable sensing performance, achieving an optimal sub-pattern assignment
(OSPA) less than 60 cm, while keeping communication services to UEs with a
reduction of the communication capacity in the order of 10% to 20%. The impact
of the number of BSs engaged in sensing is also examined, and we show that in
the specific case study, 3 BSs ensure a localization error below 1 m
Radar Technology
In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
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