CIR Parametric Rules Precocity For Ranging Error Mitigation In IR-UWB

The cutting-edge technology to support high ranging accuracy within the indoor environment is Impulse Radio Ultra Wide Band (IR-UWB) standard. Besides accuracy, IR-UWB’s low-complex architecture and low power consumption align well with mobile devices. A prime challenge in indoor IR-UWB based localization is to achieve a position accuracy under non-line-of-sight (NLOS) and multipath propagation (MPP) conditions. Another challenge is to achieve acceptable accuracy in the conditions mentioned above without any significant increase in latency and computational burden. This dissertation proposes a solution for addressing the accuracy and reliability problem of indoor localization system satisfying acceptable delay or computational complexity overhead. The proposed methodology is based on rules for identification of line-of-sight (LOS) and NLOS and the range error bias estimation and correction due to NLOS and MPP conditions. The proposed methodology provides accuracy for two major application domains, namely, wireless sensor networks (WSNs) and indoor tracking and navigation (ITN). This dissertation offers two different solutions for the localization problem. The first solution is a rules-based classification of LOS / NLOS and geometric-based range correction for WSN. In the first solution, the Boolean logic based classification is designed for identification of LOS/NLOS. The logic is based on channel impulse response (CIR) parameters. The second solution is based on fuzzy logic. The fuzzy based solution is appealing well for the stringent precision requirements in ITN. In this solution, the parametric Boolean logic from the first solution is converted and expanded into rules. These rules are implemented into a fuzzy logic based mechanism for designing a fuzzy inference system. The system estimates the ranging errors and correcting unmitigated ranges. The expanded rules and designed methodology are based on theoretical analysis and empirical observations of the parameters. The rules accommodate the parameters uncertainties for estimating the ranging error through the relationship between the input parameters uncertainties and ranging error using fuzzy inference mechanism. The proposed solutions are evaluated using real-world measurements in different indoor environments. The performance of the proposed solutions is also evaluated in terms of true classification rate, residual ranging errors’ cumulative distributions and probability density distributions, as well as outage probabilities. Evaluation results show that the true classification rate is more than 95%. Moreover, using the proposed fuzzy logic based solution, the residual errors convergence of 90% is attained for error threshold of 10 cm, and the reliability of the localization system is also more than 90% for error threshold of 15 cm

Position estimation for IR-UWB systems using compressive sensing

Recently, a growing interest in precise indoor wireless locating systems has been observed. Indoor environments are typically complex wireless propagation channels with numerous multi-paths created by closely spaced scattering objects. The ability to resolve these multi-paths is very important for good ranging resolution and positioning accuracy. Impulse-Radio Ultra-Wideband (IRUWB) is a promising technology to fulfill these requirements in harsh indoor propagation environments due to its great time resolution and immunity to multipath fading. One of the major IRUWB signal processing challenges is the high sampling demands of IR-UWB digital receivers, which greatly elevates the cost and power consumption of IR-UWB systems . Compressive Sensing provides a solution by allowing them to sample IR-UWB signals at a lower rate than the Nyquist sampling limit. The CS approach relies on the fact sparse representations are possible in the localization context. Basically two sparsity patterns can be exploited: Firstly, transmitting an ultra-short pulse through a multipath UWB channel leads to a received UWB signal that can be approximated by a linear combination of a few atoms from a pre-defined dictionary, yielding thus a sparse representation of the received UWB signal. Secondly, the inherent spatial sparsity of scene can be introduced through the use of an overcomplete basis or dictionary that enables to jointly evaluate all multiple location hypothesis. In this degree thesis, three novel data-acquisition and positioning methods exploiting different sparse representations for IR-UWB signals under challenging indoor environments are presented. Essentially, through the formulation of sparsity-based reconstruction techniques it is viable to localize targets while reducing the computational load and sampling requirements. Their performance is assessed and compared under the framework of the IEE.802.15.14a channel models, which is a standard developed specifically for UWB wireless positioning.Recientemente, se ha observado un interés creciente en los sistemas de localización pasiva inalámbrica para edificios interiores como oficinas o naves industriales. Típicamente, los ambientes de interiores son canales de propagación inalámbricos complejos con numerosas reflexiones creadas por objetos dispersivos muy próximos entre sí. La capacidad de resolver estos múltiples caminos es muy importante para una buena resolución de alcance y precisión de posicionamiento. Impulso-radio de banda ultra-ancha (UWB-IR) es una tecnología prometedora para cumplir con estos requisitos en entornos de propagación interiores debido a su gran resolución temporal y la inmunidad al desvanecimiento por múltiples caminos. Uno de los principales retos de procesamiento de señales IR-UWB es la alta demanda de muestreo de receptores digitales IRUWB, lo que eleva considerablemente el costo y el consumo de energía de los sistemas IR-UWB. Compressive Sensing proporciona una solución que permite muestrear señales IR-UWB a un ritmo menor que el límite de muestreo propuesto por Nyquist. El enfoque de este problema con Compressive Sensing se basa en el hecho de que representaciones dispersas son posibles en el contexto de la localización. Básicamente dos patrones de dispersión pueden ser explotados: En primer lugar, la transmisión de un pulso ultra corto, través de un canal de banda ancha donde la señal experimenta trayectos múltiples, conduce a una señal de UltraWideband recibida que puede ser aproximada por una combinación lineal de unos pocos átomos de un diccionario predefinido, obteniéndose así una representación dispersa de la señal de UWB recibida. En segundo lugar, la escasez de objetivos a localizar de la escena se puede utilizar mediante el uso de un diccionario sobre-completo que permita evaluar conjuntamente las múltiples hipótesis de ubicación en un escenario bidimensional, adquiriendo así una representación dispersa, con pocos elementos. En este proyecto final de carrera, se presentan tres nuevos métodos de adquisición de datos y posicionamiento que explotan diferentes representaciones dispersas para señales IR-UWB bajo ambientes interiores. En esencia se plantea, mediante la formulación de técnicas de reconstrucción de Compressive Sensing, que es viable localizar objetivos y al mismo tiempo reducir los requisitos de carga computacional y altos ritmos de muestreo. El rendimiento de los algoritmos propuestos se evalúa y se compara en el marco de los modelos de canal IEE.802.15.14a, que es un estándar desarrollado específicamente para el posicionamiento inalámbrico en sistemas UltraWideband.Recentment, s'ha observat un interès creixent en els sistemes de localització passiva sense fil per a edificis interiors com oficines o naus industrials. Típicament, els ambients d'interiors són canals de propagació complexos amb nombroses reflexions creades per objectes dispersius molt pròxims entre si. La capacitat de resoldre aquests múltiples camins és molt important per a una bona resolució d'abast i precisió de posicionament. Impuls-ràdio de banda ultra-ampla (UWB-IR) és una tecnologia prometedora per complir amb aquests requisits en entorns de propagació interiors a causa de la seva gran resolució temporal i la immunitat al esvaniment per múltiples camins. Un dels principals reptes de processament de senyals IR-UWB és l'alta demanda de mostreig dels receptors digitals IR-UWB, el que eleva considerablement el cost i el consum d'energia dels sistemes IR-UWB. Compressive Sensing proporciona una solució en la qual permet mostrejar senyals IR-UWB a un ritme menor que el límit de mostreig proposat per Nyquist. L'enfocament d'aquest problema amb Compressive Sensing es basa en el fet que representacions disperses són possibles en el context de la localització. Bàsicament dos patrons de dispersió poden ser explotats: En primer lloc, la transmissió d'un pols de molt poca duració a través d'un canal de banda ample on la senyal experimenta múltiples trajectes, això condueix a una senyal de UltraWideband rebuda que pot ser aproximada per una combinació lineal d'uns pocs àtoms d'un diccionari predefinit, obtenint-se així una representació dispersa. En segon lloc, l'escassetat de objectius a localitzar en l?escena es pot utilitzar mitjançant l'ús d'un diccionari sobre-complet que permeti avaluar conjuntament les múltiples hipòtesis d'ubicació en un escenari bidimensional, adquirint així una representació dispersa. En aquest projecte final de carrera, es presenten tres nous mètodes d'adquisició de dades i posicionament que exploten diferents representacions disperses per senyals IR-UWB sota ambients interiors. En essència es planteja, mitjançant la formulació de tècniques de reconstrucció de Compressive Sensing, que és viable localitzar objectius i al mateix temps reduir els requisits de càrrega computacional i alts ritmes de mostreig. El rendiment dels algoritmes proposats s'avalua i es comparen en el marc dels models de canal IEE.802.15.14a, que és un estàndard desenvolupat específicament per al posicionament sense fil en sistemes UltraWideband

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

Models for Efficient Automated Site Data Acquisition

Accurate and timely data acquisition for tracking and progress reporting is essential for efficient management and successful project delivery. Considerable research work has been conducted to develop methods utilizing automated site data acquisition for tracking and progress reporting. However, these developments are challenged by: the dynamic and noisy nature of construction jobsites; the indoor localization accuracy; and the data processing and extraction of actionable information. Limited research work attempted to study and develop customized design of wireless sensor networks to meet the above challenges and overcome limitations of utilizing off-the-shelf technologies. The objective of this research is to study, design, configure and develop fully customized automated site data acquisition models, with a special focus on near real-time automated tracking and control of construction operations embracing cutting edge innovations in wireless and remote sensing technologies. In this context, wireless and remote sensing technologies are integrated in two customized prototypes to monitor and collect data from construction jobsites. This data is then processed and mined to generate meaningful and actionable information. The developed prototypes are expected to have wider scope of applications in construction management, such as improving construction safety, monitoring the condition of civil infrastructure and reducing energy consumption in buildings. Two families of prototypes were developed in this research; Sensor Aided GPS (SA-GPS) prototype, which is designed and developed for tracking outdoor construction operations such as earthmoving; and Self-Calibrated Wireless Sensor Network (SC-WSN), which is designed for indoor localization and tracking of construction resources (labor, materials and equipment). These prototypes along with their hardware and software are encapsulated in a computational framework. The framework houses a set of algorithms coded in C# to enable efficient data processing and fusion that support tracking and progress reporting. Both the hardware prototypes and software algorithms were progressively tested, evaluated and re-designed using Rapid Prototyping approach. The validation process of the developed prototypes encompasses three steps; (1) simulation to validate the prototypes’ design virtually using MATLAB, (2) laboratory experiments to evaluate prototypes’ functionality in real time, and (3) testing on scaled case studies after fine-tuning the prototype design based on the results obtained from the first two steps. The SA-GPS prototype consists of a microcontroller equipped with GPS module as well as a number of sensors such as accelerometer, barometric pressure sensor, Bluetooth proximity and strain gauges. The results of testing the developed SA-GPS prototype on scaled construction jobsite indicated that it was capable of estimating project progress within 3% mean absolute percentage error and 1% standard deviation on 16 trials, in comparison to the standalone GPS which had approximately 12% mean absolute percentage error and 2% standard deviation. The SC-WSN prototype incorporates two main features. The first is the use of the Kalman filtering and smoothing for the RSSI signal to provide more stable and predictable signal for estimating the distance between a reader and a tag. The second is the use of a developed dynamic path-loss model which continually optimizes its parameters to cope with the dynamically changing construction environment using Particle Swarm Optimization (PSO) algorithm. The laboratory testing indicated the improvement in location estimation, where the produced location estimates using SC_WSN had an average error of 0.66m in comparison to 1.67m using the raw RSSI signal. Also the results indicated 60% accuracy improvement in estimating locations using the developed dynamic model. The developed prototypes are not only expected to reduce the risk of project cost and duration overruns by timely and early detection of deviations from project plan, but also enables project managers to observe and oversee their project’s status in near real-time. It is expected that the accuracy of the developed hardware, can be achieved on large-scale real construction projects. This is attributed to the fact that the developed prototype does not require any scalable improvements on its hardware technology, nor does it require any additional computational changes to its developed algorithms and software

Reaaliaikainen sis¨atilapaikannus rakennusty¨omaalla k¨aytt¨aen BLE-majakoiden trilateraatiota

A real-time indoor location tracking system prototype for construction site resource tracking was developed in this Master's Thesis. The positioning technology used was Bluetooth Low Energy Beacons and the method was trilateration. The prototype developed in this work is built upon a simpler version of a location tracking system prototype developed in iCONS research project in Aalto University. The contextual purpose of this work was to investigate in which ways a coordinate level indoor positioning system could enhance production control in construction. The lean construction philosophy is the theoretical background of this research topic. The Design Science research method was followed. The process of implementation was documented in detail. The prototype was tested in a construction site to determine the location of a person carrying a BLE beacon. The accuracy turned out to be around 10 meters at best when there was least movement. Various aspects other than accuracy have also been evaluated, and ideas for improvement are presented. The value and the applications of an ideally working coordinate level real-time location tracking system for construction production control was assessed in light of the research literature and the experience gained from creating and testing the prototype. Such a system would have a significantly positive impact on the productivity, transparency, and safety in construction.Tässä diplomityössä kehitettiin reaaliaikainen sisätilapaikannusjärjestelmä rakennustyömaan resurssien seurantaan. Paikannustekniikkana toimi Bluetooth Low Energy (BLE) -majakat ja niiden paikantaminen trilateraation avulla. Työssä kehitetty prototyyppi rakentui Aalto-yliopiston iCONS-tutkimusprojektissa kehitetyn yksinkertaisemman paikannusjärjestelmän päälle. Tässä työssä tutkittiin, millä tavoin koordinaattitason sisätilapaikannusjärjestelmä voisi parantaa tuotannonohjausta rakentamisessa. Lean-rakentaminen on tämän tutkimusaiheen teoreettinen tausta. Design Science -tutkimusmenetelmää sovellettiin tässä työssä. Menetelmän mukaisen artifaktin toteutusprosessi dokumentoitiin yksityiskohtaisesti. Prototyyppiä testattiin oikealla rakennustyömaalla BLE-majakkaa kantavan henkilön sijainnin määrittämiseksi. Tarkkuus ylsi parhaimmillaan noin 10 metriin, kun liikettä oli vähiten. Tarkkuuden lisäksi järjestelmän muita aspekteja on myös arvioitu ja parannusideoita esitetty. Ideaalin reaaliaikaisen paikannusjärjestelmän arvoa ja sovelluksia rakennusalan tuotannonohjauksessa arvioitiin sekä tutkimuskirjallisuuden että prototyypistä saadun tiedon valossa. Tällaisella järjestelmällä olisi merkittävä vaikutus rakentamisen tuottavuuteen, läpinäkyvyyteen ja turvallisuuteen

Algorithms and Methods for Received Signal Strength Based Wireless Localization

In the era of wireless communications, the demand for localization and localization-based services has been continuously growing, as increasingly smarter wireless devices have emerged to the market. Besides the already available satellite-based localization systems, such as the GPS and GLONASS, also other localization approaches are needed to complement the existing solutions. Finding different types of low-cost localization methods, especially for indoors, has become one of the most important research topics in recent years.One of the most used approaches in localization is based on Received Signal Strength (RSS) information. Specific fingerprints about RSS are collected and stored and positioning can be done through pattern or feature matching algorithms or through statistical inference. A great and immediate advantage of the RSS-based localization is its ability to exploit the already existing infrastructure of different communications networks without the need to install additional system hardware. Furthermore, due to the evident connection between the RSS level and the quality of a communications signal, the RSS is usually inherently included in the network measurements. This favors the availability of the RSS measurements in the current and future wireless communications systems.In this thesis, we study the suitability of RSS for localization in various communications systems including cellular networks, wireless local area networks, personal area networks, such as WiFi, Bluetooth and Radio Frequency Identification (RFID) tags. Based on substantial real-life measurement campaigns, we study different characteristics of RSS measurements and propose several Path Loss (PL) models to capture the essential behavior of the RSS levels in 2D outdoor and 3D indoor environments. By using the PL models, we show that it is possible to attain similar performance to fingerprinting with a database size of only 1-2% of the database size needed in fingerprinting. In addition, we study the effect of different error sources, such as database calibration errors, on the localization accuracy. Moreover, we propose a novel method for studying how coverage gaps in the fingerprint database affect the localization performance. Here, by using various interpolation and extrapolation methods, we improve the localization accuracy with imperfect fingerprint databases, such as those including substantial cover-age gaps due to inaccessible parts of the buildings

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method

Automated Site Data Acquisition for Effective Project Control

Tracking and control of construction projects depend primarily on the accuracy, frequency and time required to collect actual onsite data of construction operations that characterize the work progress. Earned Value Analysis (EVA) is being used for reporting work progress and for forecasting project status at completion and at any future time horizon. Critical to its reliable application is accurate and timely data for quantifying the budgeted cost of work performed. Automated site data acquisition has received considerable attention in recent years to circumvent the limitations of manual data collection. The limitations of existing models lie in their inability to measure the progress of different types of construction activities from inception to completion in near real-time. The objective of this research is to study and analyze the characteristics of automated data acquisition technologies in construction. This thesis is carried out with a focus on automating the process of data collection and project control. An automated model is developed that integrates project visualization-information aspects, automated site data acquisition and earned value analysis. The developed model consists of two main frameworks; one is for data acquisition and the other for data analysis and processing. Data acquisition is carried out using the integrated automated data acquisition technologies embedded in the tablet PC used in this research as well as others, referred to here as independent, automated data acquisition technologies. The developed model makes full use of 4D BIM to provide visualization and pertinent information of activities in progress. The developed model embraces the human factors to augment the visualization-information aspects, localization technologies and development of progress templates. The developed model demonstrated the significance of data fusion of a wide range of automated site data acquisition technologies and visualization-information technologies. A prime focus is placed on extensive field studies and experimental work. Field studies on construction jobsite are conducted utilizing a wide range of technologies such as 3D Scanner, RFID and GPS. Extensive experimental work is conducted to develop deployment protocols for utilization of Ultra High Frequency (UHF) passive RFID in localization and material tracking. The field and lab work resulted in a number of observations, findings, and lessons learned for deployment of passive RFID in construction. The results presented in this study demonstrate the potential use of short range RFIDs in location estimation and material tracking in a cost-effective manner for indoor construction operations. The developed method for location identification and material tracking using RFID technology can be used to obtain information required for scalable, near-real-time decision-making and timely tracking of project status. The developed methods and algorithms are implemented in prototype software. It consists of two computational frameworks; BIM+ and Control+. BIM+ is Two-Tier software, which utilizes an object-oriented BIM model. So, it can be used as an advanced tool for data acquisition through the user’s tablet PC. Control+ is Three-Tier Web-Based software for processing the captured data from the tablet PC and independent automated data acquisition technologies. The developed model, methods, algorithms and software constitute a step ahead of current progress reporting applications and expand upon automated site data acquisition technologies and visualization-information technologies use in construction