383 research outputs found
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic
cables into a large-scale strain sensing array has shown the potential to
revolutionize urban traffic monitoring by providing a fine-grained, scalable,
and low-maintenance monitoring solution. However, the real-world application of
DAS is hindered by challenges such as noise contamination and interference
among closely traveling cars. In response, we introduce a self-supervised U-Net
model that can suppress background noise and compress car-induced DAS signals
into high-resolution pulses through spatial deconvolution. Our work extends
recent research by introducing three key advancements. Firstly, we perform a
comprehensive resolution analysis of DAS-recorded traffic signals, laying a
theoretical foundation for our approach. Secondly, we incorporate space-domain
vehicle wavelets into our U-Net model, enabling consistent high-resolution
outputs regardless of vehicle speed variations. Finally, we employ L-2 norm
regularization in the loss function, enhancing our model's sensitivity to
weaker signals from vehicles in remote traffic lanes. We evaluate the
effectiveness and robustness of our method through field recordings under
different traffic conditions and various driving speeds. Our results show that
our method can enhance the spatial-temporal resolution and better resolve
closely traveling cars. The spatial deconvolution U-Net model also enables the
characterization of large-size vehicles to identify axle numbers and estimate
the vehicle length. Monitoring large-size vehicles also benefits imaging deep
earth by leveraging the surface waves induced by the dynamic vehicle-road
interaction.Comment: This preprint was re-submitted as a revised version to the IEEE
Transactions on Intelligent Transportation Systems on June 27, 202
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
Towards joint communication and sensing (Chapter 4)
Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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