Joint received signal strength, angle-of-arrival, and time-of-flight positioning

This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters

Dynamic indoor localization using maximum likelihood particle filtering

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.Fil: Wang, Wenxu. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; Chin

Tracking indoor basato su beacon: Trauma Tracker come caso di studio

L'obiettivo del progetto è la realizzazione di un sistema di tracciamento della posizione in ambienti indoor utilizzando i beacon partendo dai requisiti di Trauma Tracker, che richiede un sistema in grado di determinare la posizione corrente di una persona che si muove con un dispositivo android all'interno di un ambiente in cui sono stati posizionati dei beacon. Nel primo capitolo viene definito il tracciamento indoor, con esempi e le principali caratteristiche. Nel secondo vengono trattate le principali tecnologie utilizzate. Il terzo capitolo approfondisce la tecnologia dei beacon Bluetooth. Il quarto capitolo introduce Trauma Tracker e gli obiettivi del progetto. Il quinto capitolo si descrive la progettazione e l'implementazione di un'applicazione per il monitoraggio della posizione e dei beacon nelle vicinanze utilizzando la libreria Android Beacon Library, con i test e i risultati ottenuti. Infine nell'ultimo capitolo espone come è stata eseguita l'implementazione del suo core di tracciamento all'interno di Trauma Tracker

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