219 research outputs found
A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination
Recently with the advancement of solid state lighting and the application thereof
to Visible Light Communications (VLC), the concept of Visible Light Positioning
(VLP) has been targeted as a very attractive indoor positioning system (IPS) due to
its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth.
IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access
scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of
VLP approaches have been proposed in the literature and primarily focus on a fixed
combination of these elements and moreover evaluate the quality of the contribution
often by accuracy or precision alone.
In this dissertation, we provide a novel two-phase indoor positioning algorithmic
framework that is able to increase robustness when subject to insufficient anchor luminaries
and also incorporate any combination of the four major IPS components.
The first phase provides robust and timely albeit less accurate positioning proximity
estimates without requiring more than a single luminary anchor using time division
access to On Off Keying (OOK) modulated signals while the second phase provides a
more accurate, conventional, positioning estimate approach using a novel geometric
constrained triangulation algorithm based on angle of arrival (AoA) measurements.
However, this approach is still an application of a specific combination of IPS components.
To achieve a broader impact, the framework is employed on a collection
of IPS component combinations ranging from (1) pulsed modulations to multicarrier
modulations, (2) time, frequency, and code division multiple access, (3) received signal
strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and
triangulation positioning algorithms.
Results illustrate full room positioning coverage ranging with median accuracies
ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework
further allows for duty cycle variation to include dimming modulations and results
range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a
78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a
novel latency constrained optimization algorithm can be overlaid on the two phase
framework to decide when to simply use the coarse estimate or when to expend more
computational resources on a potentially more accurate fine estimate.
The creation of the two phase framework enables robust, illumination, latency
sensitive positioning with the ability to be applied within a vast array of system
deployment constraints
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
Võrguaja protokolli serveri arenduse ja toimivuse analüüs
Käesolev magistritöö kirjeldab lihtsa võrguaja protokolli (NTP) serveri ehitamist monoplaatarvutisse Tartu Observatooriumi aatomikella tarbeks. See selgitab NTP-protokolli terminoloogiat ja on loodud NTP-serveri ja kliendi jaoks. Mõõtmise eesmärgil kirjeldatakse ka seda, kuidas ehitatatakse NTP-klienti, mis genereerib impulssi sekundis, mis põhineb NTP-kellaajale. Selle kasutamise tulemusi võrreldakse NTP plotteri testitulemustega. Seejärel hinnatakse NTP-serveri eksperimentaalset konfiguratsiooni, millele järgneb arutelu võimalike paranduste ja tulevaste projektide üle.This Master thesis describes the process of building a simple network time protocol (NTP) server on a single-board computer for the Atomic clock at Tartu Observatory. It explains the terminology of NTP protocol and set up for the NTP server. It also describes how to build a NTP client which generates pulse per second by its own script for the measurement purposes. This new pulse is synchronized with the NTP timestamp. Results of its use are compared against the test results from NTP plotter. The experimental configuration of the NTP server is then evaluated which is followed by a discussion regarding possible improvements and future projects
Riemannian Optimization for Convex and Non-Convex Signal Processing and Machine Learning Applications
The performance of most algorithms for signal processing and machine learning applications highly depends on the underlying optimization algorithms. Multiple techniques have been proposed for solving convex and non-convex problems such as interior-point methods and semidefinite programming. However, it is well known that these algorithms are not ideally suited for large-scale optimization with a high number of variables and/or constraints. This thesis exploits a novel optimization method, known as Riemannian optimization, for efficiently solving convex and non-convex problems with signal processing and machine learning applications. Unlike most optimization techniques whose complexities increase with the number of constraints, Riemannian methods smartly exploit the structure of the search space, a.k.a., the set of feasible solutions, to reduce the embedded dimension and efficiently solve optimization problems in a reasonable time. However, such efficiency comes at the expense of universality as the geometry of each manifold needs to be investigated individually. This thesis explains the steps of designing first and second-order Riemannian optimization methods for smooth matrix manifolds through the study and design of optimization algorithms for various applications. In particular, the paper is interested in contemporary applications in signal processing and machine learning, such as community detection, graph-based clustering, phase retrieval, and indoor and outdoor location determination. Simulation results are provided to attest to the efficiency of the proposed methods against popular generic and specialized solvers for each of the above applications
State Estimation for Distributed and Hybrid Systems
This thesis deals with two aspects of recursive state estimation: distributed estimation and estimation for hybrid systems. In the first part, an approximate distributed Kalman filter is developed. Nodes update their state estimates by linearly combining local measurements and estimates from their neighbors. This scheme allows nodes to save energy, thus prolonging their lifetime, compared to centralized information processing. The algorithm is evaluated experimentally as part of an ultrasound based positioning system. The first part also contains an example of a sensor-actuator network, where a mobile robot navigates using both local sensors and information from a sensor network. This system was implemented using a component-based framework. The second part develops, a recursive joint maximum a posteriori state estimation scheme for Markov jump linear systems. The estimation problem is reformulated as dynamic programming and then approximated using so called relaxed dynamic programming. This allows the otherwise exponential complexity to be kept at manageable levels. Approximate dynamic programming is also used to develop a sensor scheduling algorithm for linear systems. The algorithm produces an offline schedule that when used together with a Kalman filter minimizes the estimation error covariance
Wi-Fi Measurement Campaign for Indoor Localization
Most of the day to day people activities are carried out inside buildings. Many sectors such as, medicine, industry, academia or even security systems require indoor positioning services. As a consequence, it is essential to develop a reliable and accurate indoor positioning system (IPS). Since global navigation satellite systems (GNSSs) are not suitable for indoor localization, several IPSs have emerged. However, each indoor positioning technology has its advantages and disadvantages. Hence, there is not an IPS system with the best performance for every situation.
The IPS databases based on the Wi-Fi infrastructure installed in two buildings of the Tampere University of Technology required an update. Therefore, the scope of this thesis has been to update and moreover, optimize the IPS fingerprint databases of these two buildings. The results have been presented and analyzed with the expectance that they will be useful for similar or wider projects.
Multiple IPSs are explained, as it is convenient to understand the advantages and the weaknesses of each technology. The technology which provides the positioning services is the fingerprint Wi-Fi received signal strength (RSS). In that way, a measurement database is built. The database is used to simulate the IPS, which is implemented through the Bayesian estimation algorithm and the k-nearest neighbors technique. Successively, the parameters of the algorithm are optimized.
The analysis of the results showed that for the lowest values of the parameters, the performance of the system improves with respect to higher values of the parameters. The best performance of the Wi-Fi based IPS results in a floor detection probability nearby 99% and an average distance error below 3 m. However, negative effects, such as the ones produced by outlier measurements, must be taken into account. Some weaknesses of the Wi-Fi based IPS, such as the challenges associated to the training phase, open a path of research that might enhance the system performance
RSSI based self-adaptive algorithms targeting indoor localisation under complex non-line of sight environments
Location Based Services (LBS) are a relatively recent multidisciplinary field which
brings together many aspects of the fields of hardware design, digital signal
processing (DSP), digital image processing (DIP), algorithm design in
mathematics, and systematic implementation. LBS provide indirect location
information from a variety of sensors and present these in an understandable and
intuitive way to users by employing theories of data science and deep learning.
Indoor positioning, which is one of the sub-applications of LBS, has become
increasingly important with the development of sensor techniques and smart
algorithms. The aim of this thesis is to explore the utilisation of indoor positioning
algorithms under complex Non-Line of sight (LOS) environments in order to meet
the requirements of both commercial and civil indoor localisation services.
This thesis presents specific designs and implementations of solutions for indoor
positioning systems from signal processing to positioning algorithms. Recently,
with the advent of the protocol for the Bluetooth 4.0 technique, which is also called
Bluetooth Low Energy (BLE), researchers have increasingly begun to focus on
developing received signal strength (RSS) based indoor localisation systems, as
BLE based indoor positioning systems boast the advantages of lower cost and
easier deployment condition. At the meantime, information providers of indoor
positioning systems are not limited by RSS based sensors. Accelerometer and
magnetic field sensors may also being applied for providing positioning
information by referring to the users’ motion and orientation. With regards to this,
both indoor localisation accuracy and positioning system stability can be
increased by using hybrid positioning information sources in which these sensors
are utilised in tandem. Whereas both RSS based sensors, such as BLE sensors,
and other positioning information providers are limited by the fact that positioning
information cannot be observed or acquired directly, which can be summarised
into the Hidden Markov Mode (HMM).
This work conducts a basic survey of indoor positioning systems, which include
localisation platforms, using different hardware and different positioning
algorithms based on these positioning platforms. By comparing the advantages of
different hardware platforms and their corresponding algorithms, a Received
Signal Strength Indicator (RSSI) based positioning technique using BLE is
selected as the main carrier of the proposed positioning systems in this research.
The transmission characteristics of BLE signals are then introduced, and the basic
theory of indoor transmission modes is detailed. Two filters, the smooth filter and
the wavelet filter are utilised to de-noise the RSSI sequence in order to increase
localisation accuracy. The theory behind these two filter types is introduced, and
a set of experiments are conducted to compare the performance of these filters.
The utilisation of two positioning systems is then introduced. A novel, off-set
centroid core localisation algorithm is proposed firstly and the second one is a
modified Monte Carlo localisation (MCL) algorithm based system. The first
positioning algorithm utilises BLE as a positioning information provider and is
implemented with a weighted framework for increasing localisation accuracy and
system stability. The MCL algorithm is tailor-made in order to locate users’ position
in an indoor environment using BLE and data received by sensors locating user
position in an indoor environment.
The key features in these systems are summarised in the following: the capacity
of BLE to compute user position and achieve good adaptability in different
environmental conditions, and the compatibility of implementing different
information sources into these systems is very high. The contributions of this
thesis are as follows: Two different filters were tailor-made for de-nosing the RSSI
sequence. By applying these two filters, the localisation error caused by small
scale fading is reduced significantly. In addition, the implementation for the two
proposed are described. By using the proposed centroid core positioning
algorithm in combination with a weighted framework, localisation inaccuracy is no
greater than 5 metres under most complex indoor environmental conditions.
Furthermore, MCL is modified and tailored for use with BLE and other sensor
readings in order to compute user positioning in complex indoor environments. By
using sensor readings from BLE beacons and other sensors, the stability and
accuracy of the MCL based indoor position system is increased further
Advanced Trends in Wireless Communications
Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics
Sensors and Systems for Indoor Positioning
This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications
- …