391 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
A Non-Line-of-Sight Mitigation Method For Indoor Ultra-Wideband Localization With Multiple Walls
Ultra-wideband (UWB) ranging techniques can provide accurate distance measurement under line-of-sight (LOS) conditions. However, various walls and obstacles in indoor non-LOS (NLOS) environments, which obstruct the direct propagation of UWB signals, can generate significant ranging errors. Due to the complex through-wall UWB signal propagation, most conventional studies simplify the ranging error model by assuming that the incidence angle is zero or the relative permittivity\u27s for different walls are the same to improve the through-wall UWB localization performance. Considering walls are different in realistic settings, this article presents a through-multiple-wall NLOS mitigation method for UWB indoor positioning. First, spatial geometric equilibrium equations of UWB through-wall propagation and a numerical method are developed for the precise modeling of UWB through-wall ranging errors. Then, calculated error maps are determined numerically without field measurements. Finally, the determined error maps are combined with a gray wolf optimization algorithm for localization. The proposed method is evaluated via field experiments with four rooms, three walls, and six penetration cases. The results demonstrate that the method can strongly mitigate the multi-wall. NLOS effects on the performance of UWB positioning systems. This solution can reduce project costs and number of power supplies for UWB indoor positioning applications
Ultra-Wideband Technology: Characteristcs, Applications and Challenges
Ultra-wideband (UWB) technology is a wireless communication technology
designed for short-range applications. It is characterized by its ability to
generate and transmit radio-frequency energy over an extensive frequency range.
This paper provides an overview of UWB technology including its definition, two
representative schemes and some key characteristics distinguished from other
types of communication. Besides, this paper also analyses some widely used
applications of UWB technology and highlights some of the challenges associated
with implementing UWB in real-world scenarios. Furthermore, this paper expands
upon UWB technology to encompass terahertz technology, providing an overview of
the current status of terahertz communication, and conducting an analysis of
the advantages, challenges, and certain corresponding solutions pertaining to
ultra-wideband THz communication
CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB
The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm
Edge inference for UWB ranging error correction using autoencoders
Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU
Map matching by using inertial sensors: literature review
This literature review aims to clarify what is known about map matching by
using inertial sensors and what are the requirements for map matching, inertial
sensors, placement and possible complementary position technology. The target
is to develop a wearable location system that can position itself within a complex
construction environment automatically with the aid of an accurate building model.
The wearable location system should work on a tablet computer which is running
an augmented reality (AR) solution and is capable of track and visualize 3D-CAD
models in real environment. The wearable location system is needed to support the
system in initialization of the accurate camera pose calculation and automatically
finding the right location in the 3D-CAD model. One type of sensor which does seem
applicable to people tracking is inertial measurement unit (IMU). The IMU sensors
in aerospace applications, based on laser based gyroscopes, are big but provide a
very accurate position estimation with a limited drift. Small and light units such
as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very
popular, but they have a significant bias and therefore suffer from large drifts and
require method for calibration like map matching. The system requires very little
fixed infrastructure, the monetary cost is proportional to the number of users, rather
than to the coverage area as is the case for traditional absolute indoor location
systems.Siirretty Doriast
UWB in 3D Indoor Positioning and Base Station Calibration
There are several technologies available for object locating and tracking in outdoor and indoor environments but performance requirements are getting tighter and precise object tracking is still largely an open challenge for researchers. Ultra wideband technology (UWB) has been identified as one of the most promising techniques to enhance a mobile node with accurate ranging and tracking capabilities. For indoor applications almost all positioning technologies require physical installation of fixed infrastructure. This infrastructure is usually expensive to deploy and maintain. The aim of this thesis is to improve the accessibility of the RF-positioning systems by lowering the configuration cost.
Real time localisation and tracking systems (RTLS) based on RF technologies pose challenges especially for the deployment of positioning system over large areas or throughout buildings within a number of rooms. If calibration is done manually by providing information about the exact position of the base stations, the initial set-up is particularly time consuming and laborious. In this thesis a method for estimating the position and orientation (x, y, z, yaw, pitch and roll) of a base station of a real time localization system is presented. The algorithm uses two-dimensional Angle of Arrival information (i.e. azimuth and elevation measurements). This allows more inaccurate manual initial survey of the base stations and improves the final accuracy of the positioning.
The thesis presents an implementation of the algorithm, simulations and empirical results. In the experiments, hardware and software procured from Ubisense was used. The Ubisense RTLS bases on UWB technology and utilises Angle of Arrival and Time Difference of Arrival techniques. Performance and functionality of the Ubisense RTLS were measured in various radio environments as well as the implementation of the calibration algorithm. Simulations and experiment studies showed that camera calibration method can be successfully adapted to position systems based on UWB technology and that the base stations can be calibrated in a sufficient accuracy. Because of more flexible calibration, the final positioning accuracy of the Ubisense system was as whole in average better.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization
Smart and unobtrusive mobile sensor nodes that accurately track their own
position have the potential to augment data collection with location-based
functions. To attain this vision of unobtrusiveness, the sensor nodes must have
a compact form factor and operate over long periods without battery recharging
or replacement. This paper presents a self-sustaining and accurate
ultra-wideband-based indoor location system with conservative infrastructure
overhead. An event-driven sensing approach allows for balancing the limited
energy harvested in indoor conditions with the power consumption of
ultra-wideband transceivers. The presented tag-centralized concept, which
combines heterogeneous system design with embedded processing, minimizes idle
consumption without sacrificing functionality. Despite modest infrastructure
requirements, high localization accuracy is achieved with error-correcting
double-sided two-way ranging and embedded optimal multilateration. Experimental
results demonstrate the benefits of the proposed system: the node achieves a
quiescent current of and operates at while performing
energy harvesting and motion detection. The energy consumption for position
updates, with an accuracy of (2D) in realistic non-line-of-sight
conditions, is . In an asset tracking case study within a
multi-room office space, the achieved accuracy level allows for identifying 36
different desk and storage locations with an accuracy of over . The
system`s long-time self-sustainability has been analyzed over in
multiple indoor lighting situations
UWB system and algorithms for indoor positioning
This research work presents of study of ultra-wide band (UWB) indoor positioning
considering different type of obstacles that can affect the localization accuracy. In the
actual warehouse, a variety of obstacles including metal, board, worker and other
obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics
package, which influence the measurement of the distance between the logistics package
and the anchor , thereby affecting positioning accuracy. A new developed method
attempts to improve the accuracy of UWB indoor positioning, through and improved
positioning algorithm and filtering algorithm. In this project, simulate the warehouse
environment in the laboratory, several simulation proves that the used Kalman filter
algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental
validation is carried out considering a mobile tag mounted on a robot platform.Este trabalho de pesquisa apresenta um estudo de posicionamento de banda ultra-larga
(UWB) em ambientes internos considerando diferentes tipos de obstáculos que podem
afetar a precisão de localização. No armazém real, uma variedade de obstáculos incluindo
metal, placa, trabalhador e outros obstáculos terão impacto NLOS (não linha de visão) no
posicionamento do pacote logístico, o que influencia a medição da distância entre o
pacote logístico e a âncora, afetando assim a precisão do posicionamento. Um novo
método desenvolvido tenta melhorar a precisão do posicionamento interno UWB, através
de um algoritmo de posicionamento e algoritmo de filtragem aprimorados. Neste projeto,
para simular o ambiente de warehouse em laboratório, diversas simulações comprovam
que o algoritmo de filtro de Kalman e o algoritmo de Markov usados podem efetivamente
reduzir o erro de NLOS. A validação experimental é realizada considerando um tag móvel
montado em uma plataforma de robô
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