353 research outputs found
A survey of deep learning approaches for WiFi-based indoor positioning
One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments
A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing
Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem
Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGPS has been a de-facto standard for outdoor positioning. For indoor positioning different
systems exist. But there is no general solution to fit all situations. A popular choice
among service provider is BLE-based IPS. BLE-has low cost, low power consumption,
and tit is are compatible with newer smartphones. These factors make it suitable for mass
market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased
IPS have advantages over its counterparts, it has not solved the position accuracy
problem yet. More research is needed to meet the position accuracy required for indoor
LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for
BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position
estimates was implemented. The first way exploits a concept from control survey called
well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle
but for in practice, triangles whose angles are greater than 30° and less than 120°
are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned.
An estimated position has the least error if the geometry from which it is estimated
satisfy well-condition. Ill-conditioned triangle should not be used for position estimation.
The proposed algorithm checked for well-condition among the closest detected
beacons and output estimates only when the beacons geometry satisfied well-condition.
The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed
algorithm did not improve on the accuracy but the variance in error was highly reduced.
The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally
gives better results but a noteworthy result from fusion was that the position estimates
during turns were accurate. When used separately, both BLE and IMU estimates showed
errors in turns. Fusion with IMU improved the accuracy. More research is required to improve
accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/):
2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results)
Towards Robust Methods for Indoor Localization using Interval Data
International audienceIndoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than state-of-the-art methods
An indoor positioning system using Bluetooth Low Energy
In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy
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
Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization
Most of the existing mobile robot localization solutions are either heavily
dependent on pre-installed infrastructures or having difficulty working in
highly repetitive environments which do not have sufficient unique features. To
address this problem, we propose a magnetic-assisted initialization approach
that enhances the performance of infrastructure-free mobile robot localization
in repetitive featureless environments. The proposed system adopts a
coarse-to-fine structure, which mainly consists of two parts: magnetic
field-based matching and laser scan matching. Firstly, the interpolated
magnetic field map is built and the initial pose of the mobile robot is partly
determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion
of prior initial pose information, the robot is localized by laser scan
matching more accurately and efficiently. In our experiment, the mobile robot
was successfully localized in a featureless rectangular corridor with a success
rate of 88% and an average correct localization time of 6.6 seconds
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