287 research outputs found
Indoor Positioning Techniques Based on Wireless LAN
As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research
Response adaptive modelling for reducing the storage and computation of RSS-based VLP
The precise (location) tracking of automated guided vehicles will be key in enlarging the productivity, efficiency and safety in the connected warehouse or production infrastructure. Combining the modest price tag, the adequate coverage and the potential centimetre accuracy makes Visible Light Positioning (VLP) systems appealing as replacements for the current, high-cost, tracking systems. Model-fingerprinting-based received signal strength (RSS) VLP enables the required accuracy. It requires an elaborate optical channel model fingerprinted in a fine-grained, and predefined positioning grid. Depending on the grid's granularity, constructing the fingerprint database demands a significant computation and storage effort. This paper employs response adaptive or sequential experimental design to form sparse channel models, vastly reducing the storage and computation. It is shown that model-fingerprinting-based RSS only requires modelling less than 1 percent of the grid points, in an elementary positioning cell. The sparse model can be re-evaluated as a way to cope with environment changeover. Model recomputation as a way of compensating for LED ageing is also studied
Self-healing radio maps of wireless networks for indoor positioning
Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real
nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente,
a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais
possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos,
sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os
sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados
para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições
conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação,
adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente
em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam
em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que
seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento
para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas
de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de
movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas
duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a
localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar
quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração
nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências
sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços
de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which
benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring,
improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be
exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and
interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they
represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi-
Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation
effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated
to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming
and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly
present in industrial environments, can be explored to help build and maintain radio maps, as long as it
is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial
vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked
using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the
self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are
proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when
Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human
effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this
solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and
analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018
Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting
Mendoza-Silva, G., Costa, A. C., Torres-Sospedra, J., Painho, M., & Huerta, J. (2022). Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting. IEEE Sensors Journal, 22(6), 4978 - 4988. https://doi.org/10.1109/JSEN.2021.3073878Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples, and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.authorsversionpublishe
Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization
Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PD
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
In this paper, we address the problem of Received Signal Strength map
reconstruction based on location-dependent radio measurements and utilizing
side knowledge about the local region; for example, city plan, terrain height,
gateway position. Depending on the quantity of such prior side information, we
employ Neural Architecture Search to find an optimized Neural Network model
with the best architecture for each of the supposed settings. We demonstrate
that using additional side information enhances the final accuracy of the
Received Signal Strength map reconstruction on three datasets that correspond
to three major cities, particularly in sub-areas near the gateways where larger
variations of the average received signal power are typically observed.Comment: 42 pages, 39 figure
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or
received signal power across a geographic area of interest using spatially
distributed sensor measurements. Applications of these maps include network
planning, interference coordination, power control, localization, and cognitive
radios to name a few. Since existing spectrum cartography techniques require
accurate estimates of the sensor locations, their performance is drastically
impaired by multipath affecting the positioning pilot signals, as occurs in
indoor or dense urban scenarios. To overcome such a limitation, this paper
introduces a novel paradigm for spectrum cartography, where estimation of
spectral maps relies on features of these positioning signals rather than on
location estimates. Specific learning algorithms are built upon this approach
and offer a markedly improved estimation performance than existing approaches
relying on localization, as demonstrated by simulation studies in indoor
scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on
Signal Processin
Advanced Wireless Localisation Methods Dealing with Incomplete Measurements
Positioning techniques have become an essential part of modern engineering, and the improvement in computing devices brings great potential for more advanced and complicated algorithms. This thesis first studies the existing radio signal based positioning techniques and then presents three developed methods in the sense of dealing with incomplete data. Firstly, on the basis of received signal strength (RSS) location fingerprinting techniques, the Kriging interpolation methods are applied to generate complete fingerprint databases of denser reference locations from sparse or incomplete data sets, as a solution of reducing the workload and cost of offline data collection. Secondly, with incomplete knowledge of shadowing correlation, a new approach of Bayesian inference on RSS based multiple target localisation is proposed taking advantage of the inverse Wishart conjugate prior. The MCMC method (Metropolis-within-Gibbs) and the maximum a posterior (MAP) / maximum likelihood (ML) method are then considered to produce target location estimates. Thirdly, a new information fusion approach is developed for the time difference of arrival (TDOF) and frequency difference of arrival (FDOA) based dual-satellite geolocation system, as a solution to the unknown time and frequency offsets. All proposed methods are studied and validated through simulations. Result analyses and future work directions are discussed
Dynamic spatial segmentation strategy based magnetic field indoor positioning system
In this day and age, it is imperative for anyone who relies on a mobile device to
track and navigate themselves using the Global Positioning System (GPS). Such
satellite-based positioning works as intended when in the outdoors, or when the
device is able to have unobstructed communication with GPS satellites.
Nevertheless, at the same time, GPS signal fades away in indoor environments due
to the effects of multi-path components and obstructed line-of-sight to the
satellite. Therefore, numerous indoor localisation applications have emerged in
the market, geared towards finding a practical solution to satisfy the need for
accuracy and efficiency.
The case of Indoor Positioning System (IPS) is promoted by recent smart devices,
which have evolved into a multimedia device with various sensors and optimised
connectivity. By sensing the device’s surroundings and inferring its context,
current IPS technology has proven its ability to provide stable and reliable indoor
localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based
IPS). To make a trade-off between accuracy and cost, considerable attention from
many researchers has been paid to the range of infrastructure-free technologies,
particularly exploiting the earth’s magnetic field (EMF).
EMF is a promising signal type that features ubiquitous availability, location
specificity and long-term stability. When considering the practicality of this
typical signal in IPS, such a system only consists of mobile device and the EMF
signal. To fully comprehend the conventional EMF-based IPS reported in the
literature, a preliminary experimental study on indoor EMF characteristics was
carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance
sources was lowered to a minimum. In response to this finding, a new concept of
spatial segmentation is devised in this research based on magnetic anomaly (MA).
Therefore, this study focuses on developing innovative techniques based on spatial
segmentation strategy and machine learning algorithms for effective indoor
localisation using EMF.
In this thesis, four closely correlated components in the proposed system are
included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic
intensity-based spatial segmentation; (iii) weighted Naïve Bayes classification
(WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm.
Kriging interpolation-based fingerprinting map reconstructs the original observed
EMF positioning database in the calibration phase by interpolating predicted
points. The magnetic intensity-based spatial segmentation component then
investigates the variation tendency of ambient EMF signals in the new database to
analyse the distribution of magnetic disturbance sources, and accordingly,
segmenting the test site. Then, WNBC blends the exclusive characteristics of
indoor EMF into original Naïve Bayes Classification (NBC) to enable a more
accurate and efficient segmentation approach. It is well known that the best IPS
implementation often exerts the use of multiple positing sources in order to
maximise accuracy. The fused features-based kNN component used in the
positioning phase finally learns the various parameters collected in the calibration
phase, continuously improving the positioning accuracy of the system.
The proposed system was evaluated on multiple indoor sites with diverse layouts.
The results show that it outperforms state-of-the-art approaches and demonstrate
an average accuracy between 1-2 meters achieved in typical sites by the best
methods proposed in this thesis across most of the experimental environments. It
can be believed that such an accurate approach will enable the future of
infrastructure–free IPS technologies
Location prediction optimisation in WSNs using kriging interpolation
© The Institution of Engineering and Technology 2016. Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This study presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, the degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that the proposed algorithm delivers approximately 98% prediction accuracy
- …