1,162 research outputs found
Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards
Time-of-flight, i.e., the time incurred by a signal to travel from
transmitter to receiver, is perhaps the most intuitive way to measure distances
using wireless signals. It is used in major positioning systems such as GPS,
RADAR, and SONAR. However, attempts at using time-of-flight for indoor
localization have failed to deliver acceptable accuracy due to fundamental
limitations in measuring time on Wi-Fi and other RF consumer technologies.
While the research community has developed alternatives for RF-based indoor
localization that do not require time-of-flight, those approaches have their
own limitations that hamper their use in practice. In particular, many existing
approaches need receivers with large antenna arrays while commercial Wi-Fi
nodes have two or three antennas. Other systems require fingerprinting the
environment to create signal maps. More fundamentally, none of these methods
support indoor positioning between a pair of Wi-Fi devices
without~third~party~support.
In this paper, we present a set of algorithms that measure the time-of-flight
to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these
algorithms and demonstrate a system that achieves accurate device-to-device
localization, i.e. enables a pair of Wi-Fi devices to locate each other without
any support from the infrastructure, not even the location of the access
points.Comment: 14 page
Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA
Indoor localization systems have become increasingly important in a wide
range of applications, including industry, security, logistics, and emergency
services. However, the growing demand for accurate localization has heightened
concerns over privacy, as many localization systems rely on active signals that
can be misused by an adversary to track users' movements or manipulate their
measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based
indoor localization system that effectively balances accuracy and privacy.
PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that
ensures users' privacy and safeguards the integrity of their measurement data
while still achieving high accuracy. The system adopts a fingerprinting
approach to address multi-path and non-line-of-sight problems and utilizes deep
neural networks to learn the complex relationship between TDoA and location.
Evaluation in a real-world testbed demonstrates PassiFi's exceptional
performance, surpassing traditional multilateration by 128%, achieving
sub-meter accuracy on par with state-of-the-art active measurement systems, all
while preserving privacy
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
In recent years, there has been a dramatic increase in the use of unmanned
aerial vehicles (UAVs), particularly for small UAVs, due to their affordable
prices, ease of availability, and ease of operability. Existing and future
applications of UAVs include remote surveillance and monitoring, relief
operations, package delivery, and communication backhaul infrastructure.
Additionally, UAVs are envisioned as an important component of 5G wireless
technology and beyond. The unique application scenarios for UAVs necessitate
accurate air-to-ground (AG) propagation channel models for designing and
evaluating UAV communication links for control/non-payload as well as payload
data transmissions. These AG propagation models have not been investigated in
detail when compared to terrestrial propagation models. In this paper, a
comprehensive survey is provided on available AG channel measurement campaigns,
large and small scale fading channel models, their limitations, and future
research directions for UAV communication scenarios
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
D21.3 Analysis of initial results at EuWIN@CTTC
Deliverable D21.3 del projecte europeu NEWCOM#The nature of this Deliverable of WP2.1 (“Radio interfaces for next-generation wireless systems”) is mainly descriptive and its purpose is to provide a report on the status of the different Joint Research Activities (JRAs) currently ongoing, some of them being performed on the facilities that are available at EuWInPeer ReviewedPreprin
Improving a wireless localization system via machine learning techniques and security protocols
The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community
Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics
In recent years, Reinforcement Learning has been able to solve extremely complex games in simulation, but with limited success in deployment to real-world scenarios. The goal of this work is create an ecosystem in which Reinforcement Learning algorithms can be deployed onto real robots in complex games.
The ecosystem begins with the creation of a development pipeline which can be used to progressively train Reinforcement Learning Algorithms in increasingly realistic scenarios, culminating with the deployment of these algorithm onto a real system. The pipeline is paired with the novel Reinforcement Learning algorithms that are better able to adapt to new scenarios than traditional methods for autonomy and robotic planning.We implement two techniques to enable this adaptation.
First, we implement a hierarchical Reinforcement Learning architecture that uses differentiated sub-policies governed by a hierarchical controller to enable fast adaptation. Second we introduce a confidence-based training process for the hierarchical controller which improves training stability and convergence times. These algorithmic contributions were evaluated using our development pipeline
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