380 research outputs found
Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.Fil: Wang, Wenxu. Guandong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australi
A Review of pedestrian indoor positioning systems for mass market applications
In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in
using the smartphones for indoor positioning. This article is dedicated to
review the most recent and interesting smartphones based indoor navigation
systems, ranging from electromagnetic to inertia to visible light ones, with an
emphasis on their unique challenges and potential real-world applications. A
taxonomy of smartphones sensors will be introduced, which serves as the basis
to categorise different positioning systems for reviewing. A set of criteria to
be used for the evaluation purpose will be devised. For each sensor category,
the most recent, interesting and practical systems will be examined, with
detailed discussion on the open research questions for the academics, and the
practicality for the potential clients
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
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)
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
Practical implementation of a hybrid indoor localization system
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life
cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use
several techniques and technologies to estimate the position of each object, and face several
requirements such as position accuracy, security, coverage range, energy consumption and
cost. This master thesis describes a real-world scenario implementation, based on Bluetooth
Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS)
that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting
(FP). The objective is to track a target node, assuming that the object follows a linear
motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of
the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed
on the two KF outputs in order to maximize the performance. The results show that the
accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed
in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m
of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante
na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados
com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias
técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios
tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação
de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low
Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes
Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens
baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received
Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é
localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear.
Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT
e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de
maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP
original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente,
a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%,
enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente
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