293 research outputs found
Observability of Path Loss Parameters in WLAN-Based Simultaneous Localization and Mapping
Indoor positioning by means of received signal strengths has been gathering much interest since the massive presence of wireless local area networks (WLANs) in buildings. Theoretical approaches rely on the perfect knowledge of the APs' positions and propagation conditions; since this is unrealistic in real world, we estimate such knowledge as well as the building map from data by applying Simultaneous Localization and Mapping (SLAM).
In this paper we address the joint estimation of the path loss parameters, namely the transmitted power and the path loss exponent, this latter being usually approximated in the literature by the free space value. We provide examples that show the relevance of estimating both parameters and analyze observability issues from the point of view of estimation theory. The integration of the parameter estimation in a WLAN based SLAM algorithm - WiSLAM - has been carried out and the results are discussed
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
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
An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models
Twisted particle filters are a class of sequential Monte Carlo methods
recently introduced by Whiteley and Lee to improve the efficiency of marginal
likelihood estimation in state-space models. The purpose of this article is to
extend the twisted particle filtering methodology, establish accessible
theoretical results which convey its rationale, and provide a demonstration of
its practical performance within particle Markov chain Monte Carlo for
estimating static model parameters. We derive twisted particle filters that
incorporate systematic or multinomial resampling and information from
historical particle states, and a transparent proof which identifies the
optimal algorithm for marginal likelihood estimation. We demonstrate how to
approximate the optimal algorithm for nonlinear state-space models with
Gaussian noise and we apply such approximations to two examples: a range and
bearing tracking problem and an indoor positioning problem with Bluetooth
signal strength measurements. We demonstrate improvements over standard
algorithms in terms of variance of marginal likelihood estimates and Markov
chain autocorrelation for given CPU time, and improved tracking performance
using estimated parameters.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Providing location everywhere
Anacleto R., Figueiredo L., Novais P., Almeida A., Providing Location Everywhere, in Progress in Artificial Intelligence, Antunes L., Sofia Pinto H. (eds), Lecture Notes in Artificial Intelligence 7026, Springer-Verlag, ISBN 978-3-540-24768-2, (Proceedings of the 15th Portuguese conference on Artificial Intelligence - EPIA 2011, Lisboa, Portugal), pp 15-28, 2011.The ability to locate an individual is an essential part of many applications, specially the mobile ones. Obtaining this location
in an open environment is relatively simple through GPS (Global Positioning System), but indoors or even in dense environments this type of
location system doesn’t provide a good accuracy. There are already systems that try to suppress these limitations, but most of them need the
existence of a structured environment to work. Since Inertial Navigation Systems (INS) try to suppress the need of a structured environment we
propose an INS based on Micro Electrical Mechanical Systems (MEMS) that is capable of, in real time, compute the position of an individual everywhere
Collaborative Indoor Positioning Systems: A Systematic Review
Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing
steadily due to their potential to improve on the performance of their non-collaborative counterparts.
In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in
(collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is
being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of
84 works, published between 2006 and 2020, have been identified. These articles were analyzed and
classified according to the described system’s architecture, infrastructure, technologies, techniques,
methods, and evaluation. The results indicate a growing interest in collaborative positioning, and
the trend tend to be towards the use of distributed architectures and infrastructure-less systems.
Moreover, the most used technologies to determine the collaborative positioning between users are
wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time
of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended
Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the
basis of the analysis and results, several promising future research avenues and gaps in research
were identified
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 signicant 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.</p
Particle Methods for Indoor Tracking in WiFi Networks
This thesis treats the problem of positioning in WiFi networks and proposes a solution using hidden Markov models and particle lters based on sequential importance sampling with resampling. Hidden Markov models prove to be a powerful framework for this type of problem exhibiting both an intuitive and adaptive model structure. One of the diculties encountered when ltering this model is that it is increasing in dimension over time. To get around this problem in this thesis, a method called sequential importance sampling is used which makes it possible to update the estimation sequentially using the previous estimate to calculate the new one. In addition, parameter inference is conducted using expectation-maximizationbased likelihood inference in the proposed model. To estimate model parameters in a hidden Markov model smoothing is done to reconstruct the underlying state sequence given all data observations. These states are then used to calculate suf- cient statistics for the sought parameters. Finding the smoothing distribution it might be necessary to use a large number of particles in the lter to overcome degeneration of the approximation. A method called xed-lag smoothing is therefore investigated in order to get better estimates. Finally we illustrate the model and the estimation algorithms on data from a real world application
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
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