Localization System Supporting People with Cognitive Impairment and Their Caregivers

Localization systems are an important componentof Ambient and Assisted Living platforms supporting personswith cognitive impairments. The paper presents a positioningsystem being a part of the platform developed within the IONISEuropean project. The system’s main function is providing theplatform with data on user mobility and localization, whichwould be used to analyze his/her behavior and detect dementiawandering symptoms. An additional function of the system islocalization of items, which are frequently misplaced by dementiasufferers.The paper includes a brief description of system’s architecture,design of anchor nodes and tags and exchange of data betweendevices. both localization algorithms for user and item positioningare also presented. Exemplary results illustrating the system’scapabilities are also included

Indoor Localization Using Wi-Fi Signals

RÉSUMÉ Plusieurs approches ont été développées pour localiser des appareils mobiles à l'intérieur de bâtiments d’une façon précise. Certaines donnent une précision de moins d'un mètre, mais elles nécessitent des infrastructures et du matériel spécifiques. D'autres utilisent une infrastructure qui est déjà déployée, mais donnent une position avec une précision inférieure. Dans ce mémoire, nous proposons plusieurs méthodes de positionnement basées sur les mesures de l'intensité du signal reçu d'une infrastructure Wi-Fi existant. Le but de ces méthodes de positionnement est de localiser le plus précisément possible l'emplacement du dispositif mobile utilisé. La première méthode de positionnement que nous proposons transforme la puissance du signal reçue en une entité appelée signature. Cette entité caractérise chaque emplacement de l'environnement où la localisation doit être effectuée. Pour localiser l'appareil mobile, la signature calculée est jumelée avec les signatures de référence les plus représentatives et qui sont déjà enregistrées dans une base de données. Dans ce mémoire, nous proposons deux approches pour produire les signatures de référence: une empirique et une théorique. La deuxième méthode de positionnement que nous proposons dans ce mémoire est de localiser les appareils mobiles en utilisant la différence entre les mesures de puissance de signaux reçus. On a appelé cette méthode la différence de puissances des signaux reçues (RSSD). Cette méthode consiste à convertir la différence de puissances des signaux reçues en des distances et d’utiliser ces distances pour estimer la position des appareils mobiles. Ensuite, nous décrivons les expériences qui nous ont conduits à développer la méthode de traitement du signal et les algorithmes de localisation. Les algorithmes et les méthodes proposés ont conduit à un système de localisation précis qui atteint 2 mètres de précision dans 90% des cas. Les résultats actuels des systèmes proposés montrent que les emplacements estimés sont précis (moins de 2 mètres) dans un environnement fermé en utilisant la méthode des signatures et une localisation précise dans les espaces ouverts en utilisant la méthode de la RSSD. Certains endroits critiques ont besoin de plus de collecte de données et plus d'informations sur l'environnement pour atteindre le même niveau de précision. Les résultats obtenus sont décrits et discutés à l’aide de cartes et de statistiques.----------ABSTRACT Several approaches have been developed to provide an accurate estimation of the position of mobile devices inside buildings. Some of them give a precision of less than one meter but they require special infrastructure and materials. Some others use an infrastructure that is already deployed but gives a position with lower precision. In this thesis, we propose several positioning methods based on the received signal strength (RSS) measurements of an existing Wi-Fi infrastructure. The aim of these positioning methods is to locate a mobile device as accurately as possible. The first method that we propose transforms the RSS to an entity called signature. This entity characterises each location of the environment where the localization should be performed. This computed signature is matched with the most representative reference signatures already recorded in a database in order to locate the mobile device. In this thesis, we propose two approaches to produce the reference signatures: an empirical and a theoretical one. The second method that we propose in this thesis is about locating the mobile devices using the difference between the received signals strength measurements. We call this method the received signal strength difference (RSSD) method. We then describe the experiments that led us to develop the signal processing method and the localization algorithms. The algorithm proposed led to an accurate localization system that reaches 2 meters of accuracy in 90% of the cases. Current results of the proposed systems show that the estimated locations are accurate (less than 2 meters) in closed environments when using the fingerprinting method and in open spaces when using the RSSD method. Some critical locations need more collected data and more information about the environment to reach the same level of accuracy. The results obtained are described and discussed using maps and statistics

UWB system and algorithms for indoor positioning

This research work presents of study of ultra-wide band (UWB) indoor positioning considering different type of obstacles that can affect the localization accuracy. In the actual warehouse, a variety of obstacles including metal, board, worker and other obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics package, which influence the measurement of the distance between the logistics package and the anchor , thereby affecting positioning accuracy. A new developed method attempts to improve the accuracy of UWB indoor positioning, through and improved positioning algorithm and filtering algorithm. In this project, simulate the warehouse environment in the laboratory, several simulation proves that the used Kalman filter algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental validation is carried out considering a mobile tag mounted on a robot platform.Este trabalho de pesquisa apresenta um estudo de posicionamento de banda ultra-larga (UWB) em ambientes internos considerando diferentes tipos de obstáculos que podem afetar a precisão de localização. No armazém real, uma variedade de obstáculos incluindo metal, placa, trabalhador e outros obstáculos terão impacto NLOS (não linha de visão) no posicionamento do pacote logístico, o que influencia a medição da distância entre o pacote logístico e a âncora, afetando assim a precisão do posicionamento. Um novo método desenvolvido tenta melhorar a precisão do posicionamento interno UWB, através de um algoritmo de posicionamento e algoritmo de filtragem aprimorados. Neste projeto, para simular o ambiente de warehouse em laboratório, diversas simulações comprovam que o algoritmo de filtro de Kalman e o algoritmo de Markov usados podem efetivamente reduzir o erro de NLOS. A validação experimental é realizada considerando um tag móvel montado em uma plataforma de robô

ANALYSIS AND ESTIMATION OF TIME OF ARRIVAL AND RECEIVED SIGNAL STRENGTH IN WIRELESS COMMUNICATION FOR INDOOR GEOLOCATION

Analysis and estimation of the time of  arrival and received signal strength  for indoor geolocation using MATLAB describes an indoor geolocation localization  which either  use the received signal strength (RSS) or time of arrival (TOA) of the received signal as their localization  metric. Though time of arrival based systems are sensitive to the available bandwidth and also to the occurrence of undetected direct path (UDP) channel conditions which RSS based system are less sensitive to the bandwidth as more resilient to undetected conditions.  This paper demonstrate the availability of radio channel modeling techniques to eliminate the costly finger printing process in pattern recognition algorithms by introducing ray tracing (RT) assisted  by RSS and TOA based algorithms. The results in figure 8 which shows the effect of pathloss on signal reception, showing free path loss reduces when plotted with rhe height  of the building  which can be used for achieving localization. it was also disovered that path loss also contributes to signal delay, the plot in figure 12  which is a probability distribution of received signal strength at different location which detect signal at the point where maximum signal was received , this RSS at fixed positions can be used to determine  geolocation

A New Set of Wi-Fi Dynamic Line-Based Localization Algorithms for Indoor Environments

Localization is of great importance for several fields such as healthcare and security. To achieve localization, GPS technologies are common for outdoor localization but are insufficient for indoor localization. This is because the accuracy and precision of the users’ indoor locations are influenced by many factors (e.g., multipath signal propagations). As a result, the methodologies and technologies for indoor localization services need to remain continuously under development. A related challenge is the time complexity of the methodologies which impacts the performance of the mobile phones’ limited resources. To address these challenges, a new set of fingerprinting algorithms called Fingerprinting Line-Based Nearest Neighbor (FLBNN) is proposed. Furthermore, the new set is compared to other existing Nearest Neighbor-based algorithms. When the deployment of four access points is considered, the FLBNN algorithms outperform several algorithms in terms of accuracy such as Nearest Neighbor version 2, Nearest Neighbor version 4, and Soft-Range-Limited KNN by approximately 17.1%, 7.8%, and 24.1%; respectively. With regards to precision, the new set of algorithms outperforms Path-Loss-Based Fingerprint Localization (PFL) and Dual-Scanned Fingerprint Localization (DFL) by approximately 7.0% and 60.9%; respectively. Moreover, the FLBNN algorithms have a time complexity of O(t * p) where the term t is the number of deployed centroids and the term p is the number of Path Loss exponents. In addition, the new set of algorithms achieves faster run time compared to those for PFL and DFL. As a result, this Thesis improves the cost and reliability of the indoor location services

Sensitivity Analysis for Measurements of Multipath Parameters Pertinent to TOA based Indoor Geolocation

Recently, indoor geolocation technologies has been attracting tremendous attention. For indoor environments, the fine time resolution of ultra-wideband (UWB) signals enables the potential of accurate distance measurement of the direct path (DP) between a number of reference sources and the people or assets of interest. However, Once the DP is not available or is shadowed, substantial errors will be introduced into the ranging measurements, leading to large localization errors when measurements are combined from multiple sources. The measurement accuracy in undetected direct path (UDP) conditions can be improved in some cases by exploiting the geolocation information contained in the indirect path measurements. Therefore, the dynamic spatial behavior of paths is an important issue for positioning techniques based on TOA of indirect paths. The objectives of this thesis are twofold. The first is to analyze the sensitivity of TOA estimation techniques based on TOA of the direct path. we studied the effect of distance, bandwidth and multipath environment on the accuracy of various TOA estimation techniques. The second is to study the sensitivity of multipath parameters pertinent to TOA estimation techniques based on the TOA of the indirect paths. We mainly looked into the effect of distance, bandwidth, threshold for picking paths, and multipath environment on the number of multipath components(MPCs) and path persistency. Our results are based on data from a new measurement campaign conducted on the 3rd floor of AK laboratory. For the TOA estimation techniques based on DP, the line of sight (LOS) scenario provides greatest accuracy and these TOA estimation techniques are most sensitive to bandwidth availability in obstructed line of sight (OLOS) scenario. All the TOA estimation algorithms perform poorly in the UDP scenario although the use of higher bandwidth can reduce the ranging error to some extent. Based on our processed results, The proposal for selecting the appropriate TOA estimation technique with certain constrains is given. The sensitivity study of multipath parameters pertinent to indirect-path-based TOA estimation techniques shows that the number of MPCs is very sensitive to the threshold for picking paths and to the noise threshold. It generally decreases as the distance increase while larger bandwidth always resolves more MPCs. The multipath components behave more persistently in line of sight (LOS) and obstructed line of sight (OLOS) scenarios than in UDP scenarios, and the use of larger bandwidth and higher threshold for picking paths also result in more persistent paths

Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements.This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation.This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms