This thesis compares and evaluates the accuracy of two RSSI-based tri-lateration methods in an indoor setting, implementing either Wi-Fi andBluetooth Low Energy (BLE) while using commercially available hardware.The purpose of evaluation is part of the long-term vision of improving thesafety of workers in adverse environments such as factories, by providing awearable Indoor Positioning System where other systems like GPS are notsuitable due to signal obstruction. Within a confined space replicating in-tended real-world conditions in terms of signal attenuation and adversity,30 consecutive measurements of signal strength readings (RSSI) to threereference nodes were collected at 10 randomized sample positions, andwas repeated across 5 tests. The accuracy of trilateration was evaluatedusing an averaged Root Mean Square Error (RMSE) over the five tests. Itwas observed that RSSI using Wi-Fi achieved better accuracy of predictingthe actual position within the testing environment than signal-strength us-ing BLE, with Wi-Fi and BLE achieving an accuracy of 0.88 and 1.85 metersrespectively. However, because of the power efficiency of BLE it is a viablecandidate for a future low-cost and device-based Indoor Localization Sys-tem to potentially be used and worn by workers. The results while alignedwith similar existing literature, infer what a low-cost indoor positioningsystem might achieve. Future research with the goal of developing suchsolutions could benefit from implementing both Wi-Fi and BLE as the basisof signal strength trilateration.

Modern indoor positioning systems encounter challenges inherent to indoor environments. Signal changes can stem from various factors like object movement, signal propagation, or obstructed line of sight. This thesis explores a supervised machine learning approach that integrates Bluetooth Low Energy (BLE) and inertial sensor data to achieve consistent angle and distance estimations. The method relies on BLE angle estimations and signal strength alongside additional sensor data from an Inertial Measurement Unit (IMU). Relevant features are extracted and a supervised learning model is trained and then validated on familiar environment tests. The model is then gradually introduced to more unfamiliar test environments, and its performance is evaluated and compared accordingly. This thesis project was conducted at the u-blox office and presents a comprehensive methodology utilizing their existing hardware. Several extensive experiments were conducted, refining both data collection procedures and experimental setups. This iterative approach facilitated the improvement of the supervised learning model, resulting in a proposed model architecture based on transformers and convolutional layers. The provided methodology encompasses the entire process, from data collection to the evaluation of the proposed supervised learning model, enabling direct comparisons with existing angle estimation solutions employed at u-blox. The results of these comparisons demonstrate more accurate outcomes compared to existing solutions when validated in familiar environments. However, performance gradually declines when introduced to a new environment, encountering a wider range of signal conditions than the supervised model had trained on. Distance estimations are then compared with the path loss propagation equation, showing an overall improvement. / Moderna inomhuspositioneringssystem möter utmaningar som förekommer i inomhusmiljöer. Signalförändringar kan bero på olika faktorer som objektets rörelse, signalutbredning eller blockerad siktlinje. Denna kandidat avhandling undersöker ett övervakat maskininlärningssätt som integrerar Bluetooth Low Energy (BLE) och tröghetssensorer för att uppnå konsekventa vinkel- och avståndsberäkningar. Metoden bygger på BLE-vinkelberäkningar och signalstyrka tillsammans med ytterligare sensordata från en Inertial Measurment Unit (IMU). Relevanta funktioner extraheras och en övervakad inlärningsmodell tränas och valideras sedan på tester i bekanta miljöer. Modellen introduceras sedan gradvis till mer obekanta testmiljöer, och dess prestanda utvärderas och jämförs därefter. Detta examensarbete genomfördes på u-blox kontor och presenterar en omfattande metodik som utnyttjar deras befintliga hårdvara. Flera omfattande experiment genomfördes, vilket förfinade både datainsamlingsprocedurer och experimentuppsättningar. Detta iterativa tillvägagångssätt underlättade förbättringen av den övervakade inlärningsmodellen, vilket resulterade i en föreslagen modellarkitektur baserad på transformatorer och konvolutionella lager. Den tillhandahållna metodiken omfattar hela processen, från datainsamling till utvärdering av den föreslagna övervakade inlärningsmodellen, vilket möjliggör direkta jämförelser med befintliga vinkelberäkningslösningar som används på u-blox. Resultaten av dessa jämförelser visar mer exakta resultat jämfört med befintliga lösningar när de valideras i bekanta miljöer. Dock minskar prestandan gradvis när den introduceras till en ny miljö, där den möter ett bredare spektrum av signalförhållanden än vad inlärningsmodellen har tränats på. Avståndsberäkningar jämförs sedan med en matematisk formel, kallat path loss propagation ekvationen, som ger distans som en funktion av uppmätt signalstyrka.

Alternate realities have fascinated mankind since early prehistory and with the advent of the computer and the smartphone we have seen the rise of many different categories of alternate reality that seek to augment, diminish, mix with or ultimately replace our familiar real world in order to expand our capabilities and our understanding. This thesis presents parallel reality as a new category of alternate reality which further addresses the vacancy problem that manifests in many previous alternate reality experiences. Parallel reality describes systems comprising two environments that the user may freely switch between, one real and the other virtual, both complete unto themselves. Parallel reality is framed within the larger ecosystem of previously explored alternate realities through a thorough review of existing categorisation techniques and taxonomies, leading to the introduction of the combined Milgram/Waterworth model and an extended definition of the vacancy problem for better visualising experience in alternate reality systems. Investigation into whether an existing state of the art alternate reality modality (Situated Simulations) could allow for parallel reality investigation via the Virtual Time Windows project was followed by the development of a bespoke parallel reality platform called Mirrorshades, which combined the modern virtual reality hardware of the Oculus Rift with the novel indoor positioning system of IndoorAtlas. Users were thereby granted the ability to walk through their real environment and to at any point switch their view to the equivalent vantage point within an immersive virtual environment. The benefits that such a system provides by granting users the ability to mitigate the effects of the extended vacancy problem and explore parallel real and virtual environments in tandem was experimentally shown through application to a use case within the realm of cultural heritage at a 15th century chapel. Evaluation of these user studies lead to the establishment of a number of best practice recommendations for future parallel reality endeavours.

Received Signal Strength Indication (RSSI) fingerprinting is a popular technique in the fieldof indoor positioning. Many studies on the subject exist acknowledging Wi-Fi signal variationconnected to Wi-Fi signals, but does not discuss possible signal variation created byconnected devices nor consequential precision loss.Understanding more about the origins of signal variation in received signal strength indication(RSSI) fingerprinting would help deal with or prevent them as well as provide moreknowledge for applications based on such signals. Environments with a varying number ofconnected devices would benefit from knowing changes in localization precision resultingfrom the devices connecting and disconnecting from the access point because it wouldindicate whether workarounds for such circumstances would be necessary.To address this issue, the work presented here focuses on how the precision of RSSIfingerprinting vary given different levels of connected Wi-Fi devices. It was carried out byconducting real world experiments at times of low- and normal levels of connected devices toaccess points on two separate locations and evaluating precision changes between statedactivity levels. These experiments took place at the University of Borås as well as at Ericssonin Borås.Experimental findings indicate that the accuracy does deteriorate in higher levels of activitythan in low activity, even though not enough evidence to determine the precision ofdeterioration. The experiments thereby provide a foundation for location-based applicationsand services that can communicate the level of positional error that exist in differentenvironments which would make the users aware but also make the applications adaptaccordingly to different environments. Based on the precision achieved, we identify variousapplications that would benefit from our proposed model. These were applications that wouldtrack mobile resources, find immobile resources, find the movement flows of users as well asnavigation- and Wi-Fi coverage applications.Further research for investigating the exact correlation between access point stress andprecision loss is proposed to fully understand the implications connected devices have onRSSI fingerprinting.

This thesis explores the creation and setup of a prototype that allows users of the device to interact within an indoor real world environment and a virtual environment simultaneously using high-tech common technology. The prototype is comprised of a small mobile device such as a cellular mobile phone, Raspberry Pi computer, a battery powered handheld Pico projector, and software developed for the Android OS. The software can easily be ported to other mobile and non-mobile operating systems. The mobile device must contain accelerometer, magnetometer, and gyroscope embedded sensors as well as 802.11 wireless network chip. The prototype software implements an indoor positioning system to track the current location and orientation of the prototype device in real time. It also displays a virtual world projection upon the surfaces of the real world in relation to the prototype’s physical location and orientation. Three different orientation estimation methods were tested and compared in this thesis. Accelerometer and magnetometer based method, gyroscope based method, and a combined method using a technique called sensor fusion were implemented. A multilateration approach was used for location estimation. Location estimates were calculated from the measured received signal strength of multiple 802.11 wireless network access points. The location of all wireless access points were known and fixed. Received signal strength data was converted to meters using a log distance propagation model, and tests were conducted to compare actual distance with converted distance. Tests were also conducted to compare multilateration estimates from unfiltered or raw RSS and filtered RSS data using a Kalman filter.

Because of the limitation of GPS indoors there is a demand for alternative methods to accurately determine both position and orientation. Previous attempts at positional tracking has required an infrastructure of hardware and sensors to provide the path of an object or person. This is not a mobile solution to a mobile problem. This project aims to answer the question if it is possible to use an Inertial measurement unit sensor for this application. It will also create a prototype device that will demonstrate the capabilities of the proposed method. The goal of the project is to reach an accuracy of ±20 cm for position and ±5 degrees for rotation. A Kalman filter will be used to filter the output from the sensor in order to get more stable and accurate readings. The results show that it is possible to determine position of ±20 cm up to 100 cm with the proposed method. An inertial measurement unit is capable of measuring rotation accuracy of ±5 degrees and a prototype has been designed and manufactured to demonstrate the method.

Recently, there has been an enormous growth of interests in geolocation applications that demand an accurate estimation of the user’s location in indoor areas. The traditional geolocation system, GPS, which was designed for being used in outdoor environments, does not perform well in indoor areas, causing frequent inaccuracies in location estimation. Therefore the need for more accurate positioning systems and even positioning techniques is a motivation for researchers to turn their attention into indoor positioning systems. In this thesis we present a unique testbed for indoor geolocation system’s real-time performance evaluation. Then we present a real-time performance evaluation of a sample indoor positioning system. We make a comparison between the simulated results of the performance evaluation of the positioning engine and the real-time performance evaluation of the positioning system. Finally, we perform a sensitivity analysis for Ekahauâ„¢ indoor positioning engine. We show that the simulation with the introduced testbed yields the same results as one would obtain by evaluating the performance of the positioning system by means of massive measurement campaigns. Running the testbed for several measurement campaigns for different scenarios enabled us to compare the results and study the effect of selected parameters on the performance of the positioning system. We also perform primitive error analysis in terms of distance error to verify the validity of the result obtained with the testbed. We show that under the same configuration both real-time performance evaluation and simulated performance evaluation will yield same result with respect to position error. We also use error modeling to determine which error model is best matched to the observed indoor positioning error. Amongst all of the possibilities of choosing methods of positioning, we focused on the Received Signal Strength (RSS) based method along with fingerprinting. Briefly said, profiles previously gathered by measurement or simulation will decide on the location of mobile terminal if a new profile comes in. It is worth mentioning that previous work similar to this testbed has been done for outdoor areas according to Ekahau's white paper. Their work is mainly focused on outdoor environment, in which multipath does not exist. In this research effort we tried to analyze the effect of different parameters on sensitivity of indoor positioning systems who suffer from multipath. Different setups for simulating real-time radio channels have been studied in literature, but still not focused on indoor areas.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015. / Navigation systems are known to provide time and location information for easy and accurate navigation in a specified environment. While Global Positioning System (GPS) has recorded a considerable success for navigating outdoors, the absence of GPS indoors has made orientation in an indoor environment challenging. Furthermore, existing technologies and methods of indoor positioning and navigation, such as WLAN, Bluetooth and Infrared, have been complex, inaccurate, expensive and challenging to implement; thereby limiting the usability of these technologies in less developed countries. This limitation of navigation services makes it difficult and time consuming to locate a destination in indoor and closed spaces. Hence, recent works with Near Field Communication (NFC) has kindled interest in positioning and navigation. While navigating, users in less developed nations face several challenges, such as infrastructure complexity, high-cost solution, inaccuracy and usability. However, this research focuses on providing interventions to alleviate usability challenges, in order to strengthen the overall accuracy and the navigation effectiveness in stringent environments through the experiential manipulation of technical attributes of the positioning and navigation system in indoor environments. Therefore, this study adopted the realist ontology and the positivist epistemological approach. It followed a quantitative and experimental method of empirical enquiry, and software engineering and synthesis research methods. The study entails three implementation processes, namely map generation, positioning framework and navigation service using a prototype mobile navigation application that uses the NFC technology. It used open-source software and hardware engineering tools, instruments and technologies, such as Ubuntu Linux, Android Software Development Kit, Arduino, NFC APIs and PandaBoard. The data was collected and the findings evaluated in three stages: pre-test, experiment and post-test.

No Brasil existem milhões de brasileiros com algum tipo de dificuldade de locomoção, sendo que grande parte dessas pessoas com deficiência necessita de cadeira de rodas para se locomover. Com o fato de existirem inúmeros de brasileiros cadeirantes, o desenvolvimento de tecnologias ditas ¨assistivas¨, ou seja, tecnologias que visam assistir estas pessoas em suas atividades, tem recebido uma crescente atenção, tanto do ponto de vista de negócios (com o aumento de produtos), como de pesquisas acadêmicas. Dentre estas linhas de pesquisa, a integração entre cadeiras de rodas automatizadas com sistemas de automação predial e residencial está entre as linhas de pesquisa de maior destaque, pois tem como objetivo tornar a vida do usuário de cadeira de rodas mais produtiva e cômoda, contribuindo para sua independência. Os sistemas de automação predial e residencial mais complexos elevam o nível de um ambiente automatizado para um ambiente inteligente, pois tornam o nível de abstração maior, fornecendo serviços de forma pervasiva. O ambiente inteligente necessita de informações para que possa oferecer seus serviços ao cadeirante, e uma informação importante seria a localização da cadeira de rodas automatizada. Neste contexto, este trabalho propõe um sistema de localização em interiores para cadeiras de rodas automatizadas em ambientes inteligentes. A proposta combina Redes de Sensores Sem Fio (RSSF) e a tecnologia RFID, onde as etiquetas RFID são utilizadas como marcos de referências para a calibração automática dos parâmetros da RSSF, que convencionalmente são obtidos de forma experimental. O estudo de caso para validar o sistema de localização proposto foi realizado numa sala automatizada no prédio da Engenharia Elétrica da Universidade Federal do Rio Grande do Sul (UFRGS) como ambiente inteligente, e uma cadeira de rodas automatizada adaptada para se comunicar com a RSSF. Além deste estudo de caso, foram realizados outros experimentos com intuito de avaliar qual método geraria os melhores resultados para o sistema de localização baseado em RSSF e a tecnologia RFID, combinando diferentes modelos de propagação de sinal RF e diferentes formas de calibrar os parâmetros da rede. Além disso, outras questões sobre o sistema de localização proposto foram analisadas, como por exemplo, a influência do aumento da quantidade de nós da rede na exatidão do sistema, o comportamento do sistema quando, para um mesmo cenário, o sentido do percurso é invertido, e a influência do leiaute dos nós da rede no desempenho do sistema. / In Brazil there are millions of people with some kind of limited mobility, and that most of these disabled people need a wheelchair to get around. With the fact that there are millions of wheelchair users, the development of assistive technologies, i.e. technologies to assist these people in their activities, has received increasing attention both from the standpoint of business (with the increase of products), as academic research. Among these lines of research, the integration of automated wheelchairs with home automation systems are among the research areas of greatest importance because it aims to make the user's life more productive and comfortable, contributing to their independence. The home automation systems more complex increase the level of an automated environment for intelligent environment, as they make the higher level of abstraction, providing pervasive services. The intelligent environment needs information so it can offer your services to the wheelchair user, and important information would be the location of the automated wheelchair. Within this context, this paper proposes an indoor positioning system for wheelchairs in intelligent environments. The proposal combines Wireless Sensor Networks (WSN) and RFID technology, where RFID tags are used as landmark for automatic calibration of the parameters of WSN, which are conventionally obtained experimentally. The case study to validate the proposed indoor positioning system was performed at an automated room in the building of Electrical Engineering, Universidade Federal do Rio Grande do Sul (UFRGS) with the intelligent environment and automated wheelchair adapted to communicate with the WSN. In addition to the case study, other experiments in order to assess which method would produce the best results for the localization system based on RFID and WSN, combining different radio propagation model and different forms of calibrate the parameters of the WSN. In addition, other questions about the proposed indoor positioning system were analyzed, for example, the influence of increasing the amount of network nodes on the system accuracy, the behavior of the system for the same scenario when the direction of travel is reversed, and the influence of the layout of the network nodes on the system performance.

Tracking objects or people in an indoor environment has a wide variety of uses in many different areas, similarly to positioning systems outdoors. Indoor positioning systems operate in a very different environment however, having to deal with obstructions while also having high accuracy. A common solution for indoor positioning systems is to have three or more stationary anchor antennas spread out around the perimeter of the area that is to be monitored. The position of a tag antenna moving in range of the anchors can then be found using trilateration. One downside of such a setup is that the anchors must be setup in advance, meaning that rapid deployment to new areas of such a system may be impractical. This thesis aims to investigate the possibility of using a different setup, where three anchors are placed close together, so as to fit in a small hand-held device. This would allow the system to be used without any prior setup of anchors, making rapid deployment into new areas more feasible. The measurements done by the antennas for use in trilateration will always contain noise, and as such algorithms have had to be developed in order to obtain an approximation of the position of a tag in the presence of noise. These algorithms have been developed with the setup of three spaced out anchors in mind, and may not be sufficiently accurate when the anchors are spaced very closely together. To investigate the feasibility of such a setup, this thesis tested four different algorithms with the proposed setup, to see its impact on the performance of the algorithms. The algorithms tested are the Weighted Block Newton, Weighted Clipped Block Newton, Linear Least Squares and Non-Linear Least Squares algorithms. The Linear Least Squares algorithm was also run with measurements that were first run through a simple Kalman filter. Previous studies have used the algorithms to find an estimated position of the tag and compared their efficiency using the positional error of the estimate. This thesis will also use the positional estimates to determine the angular position of the estimate in relation to the anchors, and use that to compare the algorithms. Measurements were done using DWM1001 Ultra Wideband (UWB) antennas, and four different cases were tested. In case 1 the anchors and tag were 10 meters apart in line-of-sight, case two were the same as case 1 but with a person standing between the tag and the anchors. In case 3 the tag was moved behind a wall with an adjacent open door, and in case 4 the tag was in the same place as in case 3 but the door was closed. The Linear Least Squares algorithm using the filtered measurements was found to be the most effective in all cases, with a maximum angular error of less than 5$^\circ$ in the worst case. The worst case here was case 2, showing that the influence of a human body has a strong effect on the UWB signal, causing large errors in the estimates of the other algorithms. The presence of a wall in between the anchors and tag was found to have a minimal impact on the angular error, while having a larger effect on the spatial error. Further studies regarding the effects of the human body on UWB signals may be necessary to determine the feasibility of handheld applications, as well as the effect of the tag and/or the anchors moving on the efficiency of the algorithms.