Grâce à l’émergence dans la vie quotidienne des appareils de plus en plus populaires que sont les smartphones et les tablettes, la tâche de postionner l'utilisateur par le biais de son téléphone est une problématique fortement étudiée dans les domaines non seulement de la recherche mais également des communautés industrielles. Parmi ces technologies, les approches GPS sont devenues une norme et ont beaucoup de succès pour une localisation en environnement extérieur. Par contre, le Wi-Fi, les capteurs inertiels et le Bluetooth sont plutôt préférés pour les tâches de positionnement dans un environnement intérieur.Pour ce qui concerne le positionnement des smartphones, les approches basées sur les « empreintes digitales » (fingerprint) Wi-Fi sont bien établies. D'une manière générale, ces approches tentent d'apprendre la fonction de correspondance (cartographie) des caractéristiques du signal Wi-Fi par rapport à la position de l’appareil dans le monde réel. Elles nécessitent généralement une grande quantité de données pour obtenir une bonne cartographie. Lorsque ces données d'entraînement disponibles sont limitées, l'approche basée sur les empreintes digitales montre alors des taux d’erreurs élevés et devient moins stable. Dans nos travaux, nous explorons d’autres approches, différentes, pour faire face à cette problématique du manque de données d'entraînement. Toutes ces méthodes sont testées sur un ensemble de données public qui est utilisé lors d’une compétition internationale à la Conférence IPIN 2016.En plus du système de positionnement basé sur la technologie Wi-Fi, les capteurs inertiels du smartphone sont également utiles pour la tâche de suivi. Les trois types de capteurs, qui sont les accéléromètres, le gyroscope et la boussole magnétique, peuvent être utilisés pour suivre l'étape et la direction de l'utilisateur (méthode SHS). Le nombre d'étapes et la distance de déplacement de l'utilisateur sont calculés en utilisant les données de l'accéléromètre. La position de l'utilisateur est calculée par trois types de données avec trois méthodes comprenant la matrice de rotation, le filtre complémentaire et le filtre de Madgwick. Il est raisonnable de combiner les sorties SHS avec les sorties de Wi-Fi, car les deux technologies sont présentes dans les smartphones et se complètent. Deux approches combinées sont testées. La première approche consiste à utiliser directement les sorties Wi-Fi comme points de pivot pour la fixation de la partie de suivi SHS. Dans la deuxième approche, nous comptons sur le signal Wi-Fi pour construire un modèle d'observation, qui est ensuite intégré à l'étape d'approximation du filtre à particules. Ces combinaisons montrent une amélioration significative par rapport au suivi SHS ou au suivi Wi-Fi uniquement.Dans un contexte multiutilisateur, la technologie Bluetooth du smartphone pourrait fournir une distance approximative entre les utilisateurs. La distance relative est calculée à partir du processus de numérisation du périphérique Bluetooth. Elle est ensuite utilisée pour améliorer la sortie des modèles de positionnement Wi-Fi. Nous étudions deux méthodes. La première vise à créer une fonction d'erreur qui permet de modéliser le bruit dans la sortie Wi-Fi et la distance approximative produite par le Bluetooth pour chaque intervalle de temps spécifié. La seconde méthode considère par contre cette relation temporelle et la contrainte de mouvement lorsque l'utilisateur se déplace. Le modèle d'observation du filtre à particules est une combinaison entre les données Wi-Fi et les données Bluetooth. Les deux approches sont testées en fonction de données réelles, qui incluent jusqu'à quatre utilisateurs différents qui se déplacent dans un bureau. Alors que la première approche n'est applicable que dans certains scénarios spécifiques, la deuxième approche montre une amélioration significative par rapport aux résultats de position basés uniquement sur le modèle d'empreintes digitales Wi-Fi. / With the popularity of smartphones and tablets in daily life, the task of finding user’s position through their phone gains much attention from both the research and industry communities. Technologies integrated in smartphones such as GPS, Wi-Fi, Bluetooth and camera are all capable for building a positioning system. Among those technologies, GPS has approaches have become a standard and achieved much success for the outdoor environment. Meanwhile, Wi-Fi, inertial sensors and Bluetooth are more preferred for positioning task in indoor environment.For smartphone positioning, Wi-Fi fingerprinting based approaches are well established within the field. Generally speaking, the approaches attempt to learn the mapping function from Wi-Fi signal characteristics to the real world position. They usually require a good amount of data for finding a good mapping. When the available training data is limited, the fingerprinting-based approach has high errors and becomes less stable. In our works, we want to explore different approaches of Wi-Fi fingerprinting methods for dealing with a lacking in training data. Based on the performance of the individual approaches, several ensemble strategies are proposed to improve the overall positioning performance. All the proposed methods are tested against a published dataset, which is used as the competition data of the IPIN 2016 Conference with offsite track (track 3).Besides the positioning system based on Wi-Fi technology, the smartphone’s inertial sensors are also useful for the tracking task. The three types of sensors, which are accelerate, gyroscope and magnetic, can be employed to create a Step-And-Heading (SHS) system. Several methods are tested in our approaches. The number of steps and user’s moving distance are calculated from the accelerometer data. The user’s heading is calculated from the three types of data with three methods, including rotation matrix, Complimentary Filter and Madgwick Filter. It is reasonable to combine SHS outputs with the outputs from Wi-Fi due to both technologies are present in the smartphone. Two combination approaches are tested. The first approach is to use directly the Wi-Fi outputs as pivot points for fixing the SHS tracking part. In the second approach, we rely on the Wi-Fi signal to build an observation model, which is then integrated into the particle filter approximation step. The combining paths have a significant improvement from the SHS tracking only and the Wi-Fi only. Although, SHS tracking with Wi-Fi fingerprinting improvement achieves promising results, it has a number of limitations such as requiring additional sensors calibration efforts and restriction on smartphone handling positions.In the context of multiple users, Bluetooth technology on the smartphone could provide the approximated distance between users. The relative distance is calculated from the Bluetooth inquiry process. It is then used to improve the output from Wi-Fi positioning models. We study two different combination methods. The first method aims to build an error function which is possible to model the noise in the Wi-Fi output and Bluetooth approximated distance for each specific time interval. It ignores the temporal relationship between successive Wi-Fi outputs. Position adjustments are then computed by minimizing the error function. The second method considers the temporal relationship and the movement constraint when the user moves around the area. The tracking step are carried out by using particle filter. The observation model of the particle filter are a combination between the Wi-Fi data and Bluetooth data. Both approaches are tested against real data, which include up to four different users moving in an office environment. While the first approach is only applicable in some specific scenarios, the second approach has a significant improvement from the position output based on Wi-Fi fingerprinting model only.

Indoor positioning is desired in many areas for various reasons, such as positioning products in industrial environments, hospital equipment or firefighters inside a building on fire. One even tougher situation where indoor positioning can be useful is locating a specific object on a shelf in a commercial setting. This thesis aims to investigate and design different network deployment strategies in an indoor environment in order to achieve both high position estimation accuracy and availability. The investigation considers the two positioning techniques downlink time difference of arrival, DL-TDOA, and round trip time, RTT. Simulations of several deployments are performed in two standard scenarios which mimic an indoor open office and an indoor factory, respectively. Factors having an impact on the positioning accuracy and availability are found to be deployment geometry, number of base stations, line-of-sight conditions and interference, with the most important being deployment geometry. Two deployment strategies are designed with the goal of optimising the deployment geometry. In order to achieve both high positioning accuracy and availability in a simple, sparsely cluttered environment, the strategy is to deploy the base stations evenly around the edges of the deployment area. In a more problematic, densely cluttered environment the approach somewhat differs. The proposed strategy is now to identify and strategically place some base stations in the most cluttered areas but still place a majority of the base stations around the edges of the deployment area. A robust positioning algorithm is able to handle interference well and to decrease its impact on the positioning accuracy. The cost, in terms of frequency resources, of using more orthogonal signals may not be worth the small improvement in accuracy and availability.

Les systèmes de navigation par satellites permettent les applications de positionnement en extérieur, dont la navigation routière. Dans les environnements contraints, comme l'intérieur des bâtiments où ces signaux satellitaires sont dégradés, la continuité du service de positionnement est nécessaire. Les applications adaptées aux citoyens modernes avec leurs appareils nomades posent des contraintes fortes de mobilité, de coûts et de limitations des infrastructures existantes. Les larges possibilités de déplacements dans des environnements hétérogènes accroissent les difficultés. Un état de l'art alimenté par une décennie de travaux académiques et industriels présente un ensemble de technologies qui visent disponibilité et performance. L'accent porte ensuite sur les systèmes inertiels pédestres à bas coût, avec une première contribution permettant d'abandonner la détection de pas au profit d'une mobilité facilitée, mais reste limitée par la connaissance de la distance parcourue pendant une phase de calibration. Cette approche nouvelle est confrontée à celle classique au pied, puis éprouvée pour différents capteurs et piétons au travers d'expérimentations répétées en conditions réalistes. Une seconde contribution décline une constellation radio locale pour estimer la distance avec une infrastructure allégée à deux émetteurs. Elle s’inspire d’une conception satellitaire sur radio programmable pour faciliter sa compatibilité avec l’existant et explorer ses performances. Une surveillance du rapport signal à bruit inter-canal améliore la précision du positionnement. Le couplage de ces systèmes asynchrones et distribués est évalué en intérieur sur une plateforme automatisée / The global navigation satellite systems allow outdoor positioning applications, including car navigation. In challenging environments, such as the buildings where satellite signals are mitigated, georeferenced points of interest or navigation applications require a continuity of the positioning service. The applications adapted to modern citizens and their mobile devices raise strong constraints on mobility, costs and limitations of the existing infrastructure. The wide variety of displacements in heterogeneous environments increases the challenge. A state of the art fed by a decade of academic and industrial works presents a set of technologies that target availability and performance. The emphasis follows on the low cost pedestrian inertial systems, with a first contribution allowing to give up the step detection for the benefit of an easier mobility, but remains limited to the knowledge of the distance traveled during a calibration phase. This new approach is compared with the classical foot-mounted approach, and then benchmarked with several sensors and pedestrians through repeated experiments in real conditions. A second contribution operates a local radio constellation to estimate the distance with a minimal infrastructure with two emitters. The signals and the algorithm are based on a reproduction of satellite systems to ease the compatibility but are implemented on a programmable radio to explore the performances. A monitoring of the difference of carrier to noise ratio between the radio channels improves the distance estimation. The hybridization of these distributed, asynchronous and multi-rates inertial and radio systems is evaluated indoor on a motorized platform

Tandis que les humains explorent la nature sans relâche, ils font également attention à être plus conscients d'eux-mêmes, à mieux connaître ce qui les entourent, et, par exemple, à être informés de leurs positions, vitesses, ou trajectoires où qu'ils se trouvent. Les systèmes de positionnement par satellites (GNSS) ont fourni une manière de le faire à l'extérieur, devenant un assistant indispensable pour nous. Après le succès de GPS et GLONASS, Galileo et BeiDou sont actuellement en cours de déploiement, offrant plus de choix pour le positionnement autonome ou assisté. Toutefois, les signaux GNSS sont vulnérables aux obstacles: il n'y a presque pas de service GNSS à l'intérieur des bâtiments, dans les tunnels, ou dans les parkings souterrains; la continuité des services de positionnement par satellites n'est toujours pas assurée dans les "canyons urbains". Afin de faire face à ce problème, cette thèse est consacrée au positionnement en environnements contraints (où le GNSS n'est pas disponible), en y incluant la conception, les algorithmes et les technologies correspondantes. Un état de l'art est élaboré sur les systèmes de positionnement à radio ou à l'inertie. Parmi les technologies possibles, la discussion ne se limite pas à l'approche basée sur GNSS, mais elle est centrée sur cette dernière à cause de ses avantages et aussi de l’implication profonde de notre groupe de recherche dans ce domaine. Nous avons, d'une part, étudié la possibilité ainsi que les limites du positionnement avec une précision centimétrique en déployant notre système Repealite (i.e. un système de positionnement à l'intérieur d'une base des émetteurs GNSS spéciaux); et proposé, d'autre part, une méthode de localisation en groupe d'objets dynamiques communicants. Cette méthode procède d'une problématique délicate que l'on rencontre dans les approches GNSS, qu'est la détermination de la position initiale du récepteur. On montre en quoi elle permet également d'aller au-delà de la portée des approches classiques GNSS / While the human beings explore the nature tirelessly, they also put significant concerns to be aware of themselves, to know better of the circumstances, and to be informed with their precise positions, velocities, trajectories, and so on, in local environments. The Global Navigation Satellite Systems (GNSS) have provided an efficient method to do so outdoors, and have already become an indispensable assistant of many people. After the success of GPS and GLONASS, Galileo and BeiDou are currently under deployment, offering more choices of the independent or collaborative positioning. However, the GNSS signal is vulnerable to obstructions: almost no GNSS services are available inside buildings, tunnels, or underground parkings; the services are not always coherent in urban canyons. In order to address this problem, this thesis is dedicated to the design frameworks for the positioning in GNSS-challenged environments, as well as the corresponding algorithms and technologies. A brief survey of the latest radio-based and inertial positioning/tracking systems is provided. Among the feasible technologies, the discussion is centered on but not limited to the GNSS-based approach, which is due to the inherited advantages of this approach and also the deep engagement of our research group in this domain. We have, on one hand, explored the possibility and limitations on the centimeter-accuracy positioning with our Repealite system (i.e. a GNSS-base indoor positioning system with specific features); on the other hand, a method of batch localization for the nodes in a network of dynamic communicating objects is proposed, which is originated from an issue of the GNSS-based approach - the resolution of the receiver initial point, but then it goes beyond the scope of the “classical” GNSS-based approach

<div>Over the past few decades, the focus of wireless communication technology has been shrinking in terms of coverage area. It started with WMAN (Wireless Metropolitan Area Network), moved to WLAN (Wireless Local Area Network) and WPAN (Wireless Personal Area Network), and is soon expected to move to WBAN (Wireless Body Area Network). Wireless positioning/location services present a perfect analogy to wireless communication services. It started with the use of GPS (Global Positioning System), is moving to Local Area Positioning System (LPS) and will be soon moving to Personal and Body Area Positioning Systems (BPS) in the future.</div><div><br></div>This thesis presents the development of a high-speed and high-accuracy wireless magnetic positioning system which can locate the position and orientation of the sensor in real-time with a sub-mm level accuracy in body area. The system consists of an antenna (transmitter) and one or multiple sensors (receivers). The sensor module consists of a tri-axis AC magnetic field sensor, an orientation sensor, a micro-controller and a communication unit. The system is robust to multi-path, low-power, low-cost and provides complete location privacy to its users. Possible implementations of this technology could be in the field of gaming, media entertainment, security, robotics, bio medical, motion-capture and home-automation. The ultra-low latency of the system and its ability to track the sensor anywhere around the antenna without occlusion makes it a perfect candidate to be used as a Virtual/Augment Reality (VR/AR) input device.

Indoor positioning systems can be of a great help when trying to local- ize in an indoor environment. To be able to navigate, there is a need for an indoor map that to some degree represent the reality. This thesis aims to come up with a proof of concept of a system that maps rectan- gular objects onto a two dimensional map in an indoor environment. The maps will be drawn with the help of the position of a hand-held device that marks the corners of the objects. A problem that arises is that the error in the positioning can contribute to objects that have the same size and are aligned in reality can vary in size and be misaligned on the map. Another problem is that with the error in the position- ing, the position might end up within an existing object, which leads to a bad user experience. The problems were solved by developing a system, that given a map with objects mapped from the help of posi- tioning, can adjust the shape and positions of the objects based on the mean-values of multiple identical objects. To avoid positions within objects, a function was written that always checks if the positions is within an existing object and if that is the case, then move the correct the position to the closest position outside of the object. The final re- sult was a proof of concept of a system that can generate maps with mapped objects and users that can traverse the map with a good user experience. / Inomhuspositioneringssystem kan vara till stor hjälp när man vill lo- kalisera sig i en inomhusmiljö. För att kunna navigera sig så krävs det en inomhuskarta som i viss mån representerar verkligheten. Den- na avhandling ämnar att komma fram till en konceptvalidering för ett system som ska mappa upp rektangulära objekt på tvådimensio- nella kartor i en inomhusmiljö. Kartorna ritas upp med hjälp av po- sitionen från en handhållen enhet som markerar hörnen på objekten. Problem som uppstår då är att felet från positionen kan få objekt som är av samma storlek och uppställda på rad i verkligheten, att se oli- ka stora ut och vara placerade lite huller om buller på kartan. Ett an- nat problem är att positionen kan med det här felet i positioneringen, komma att befinna sig i ett existerande objekt, vilket leder till en dålig användarupplevelse. Problemen löstes genom att utveckla ett system, som utifrån en karta med objekt mappade med hjälp av positionering, kan justera objektens form och position baserat på medelvärdena av flertalet likadana objekt. För att undvika positioner innanför objekt så skrevs en funktion som hela tiden kollar om positionen befinner sig i något befintligt objekt och om så skulle vara fallet så korrigeras po- sitionen till närmsta position utanför objektet. Slutresultatet blev en konceptvalidering av ett system som kan generera kartor med mappa- de objekt och användare som kan traversera dessa kartor med en god användarupplevelse.

Traditional methods for locating books and resources in libraries often entail browsing catalogsor manual searching that are time-consuming and inefficient. This thesis investigates thepotential of automated digital services to streamline this process, by utilizing Wi-Fi signal datafor precise indoor localization. Central to this study is the development of a model that employsWi-Fi signal strength (RSSI) and round-trip time (RTT) to estimate the locations of library userswith arm-length accuracy. This thesis aims to enhance the accuracy of location estimation byexploring the complex, nonlinear relationship between Received Signal Strength Indicator(RSSI) and Round-Trip Time (RTT) within signal fingerprints. The model was developed usingan artificial neural network (ANN) to capture the relationship between RSSI and RTT. Besides,this thesis introduces and evaluates the performance of a novel variant of the Particle SwarmOptimization (PSO) algorithm, named Randomized Particle Swarm Optimization (RPSO). Byincorporating randomness into the conventional PSO framework, the RPSO algorithm aims toaddress the limitations of the standard PSO, potentially offering more accurate and reliablelocation estimations. The PSO algorithms, including RPSO, were integrated into the trainingprocess of ANN to optimize the network’s weights and biases through direct optimization, aswell as to enhance the hyperparameters of the ANN’s built-in optimizer. The findings suggestthat optimizing the hyperparameters yields better results than direct optimization of weights andbiases. However, RPSO did not significantly enhance the performance compared to thestandard PSO in this context, indicating the need for further investigation into its application andpotential benefits in complex optimization scenarios.

Location-based services (LBSs) have become pervasive, and the demand for these systems and services is rising. Indoor Positioning Systems (IPSs) are key to extend location-based services indoors where the Global Positioning System (GPS) is not reliable due to low signal strength and complicated signal propagation environment. Most existing IPSs either require the installation of special hardware devices or build a fingerprint map, which is expensive, time-consuming, and labor-intensive. Developments in microelectromechanical systems (MEMS) have resulted in significant advancements in the low-cost compact MARG inertial sensors, making it possible to achieve low-cost and high-accuracy IPSs. This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained indoor positioning and tracking system based on Pedestrian Dead Reckoning (PDR) using MEMS MARG inertial sensors. PDR-based systems rely on MARG inertial sensor measurements to estimate the current position of the object by using a previously determined position without external references. Many issues still exist in developing such systems, such as cumulative errors, high-frequency sensor noises, the gyro drift issue, magnetic distortions, etc. As the MARG sensors are inherently error-prone, the most significant challenge is how to design sensor fusion models and algorithms to accurately extract useful location-based information from individual motion and magnetic sensors. The objective of this thesis is to solve these issues and mitigate the challenges. The proposed positioning system is designed with four main modules at the system level and a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. Experimental evaluations show that the proposed position estimation algorithm is able to achieve high positioning accuracy at low costs for the indoor environment. / Thesis / Master of Applied Science (MASc) / With the maturity of microelectromechanical systems (MEMS) technology in recent years, Magnetic, Angular Rate, and Gravity (MARG) sensors are embedded in most smart devices. This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained MEMS MARG inertial sensor-based indoor positioning and tracking system with high precision. The proposed positioning system uses the Pedestrian Dead Reckoning (PDR) approach and includes four main modules at the system level with a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. The two modes are static mode and dynamic mode. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. The detection and estimation algorithms of each module are presented in the system design chapter. Experimental evaluations including trajectory results under five scenarios show that the proposed position estimation algorithm achieves a higher position accuracy than that of conventional estimation methods.

Denna rapport presenterar ett proof-of-concept avseende inomhuspositionering, baserat på Bluetooth-fyrar och mobiltelefoner, kombinerat med ett system för besökshantering. Detta mot bakgrunden av ett önskemål om att underlätta interaktionen mellan lärare och studenter på Malmö högskola.Till följd av att viss verksamhet på Malmö högskola flyttat till nya lokaler har lärarnas arbetsplatser förändrats, vilket lett fram till att studenterna har svårt att dels lokalisera tillgängliga lärare och dels initiera besök utan att störa andra lärare. Genom att upprätta en prioriterad kravspecifikation utifrån MoSCoW-metoden, baserat på intervju- och enkätsvar från lärare, identifieras kraven till ett system för positionering av lärare samt tillhörande system för besökshantering.Utifrån Pahl’s utvecklingsprocess för framtagning av en teknisk artefakt analyseras och utvärderas olika lösningsalternativ för inomhuspositionering, vilket leder fram till ett förslag avseende en prototyp. Prototypen avser symbolisk positionering i ett system för närhetsdetektering utifrån fördefinierade zoner, baserat på aktiva mobiltelefoner och passiva Bluetooth-fyrar. Slutligen implementeras framtagen prototyp för inomhuspositionering, i kombination med ett system för besökshantering, för att visa på dess genomförbarhet. Detta i form av en summerad systemvy som även inkluderar en molnbaserad databas, ett webbgränssnitt och två Android-applikationer. / This report presents a proof-of-concept regarding indoor positioning based on Bluetooth beacons and mobile phones, combined with a system for visitor management. This against the background of a desire to facilitate the interaction between teachers and students at Malmö University.As a result of that Malmö University moved to new facilities, the teachers' workplaces changed, which led to students having difficulty locating available teachers and initiate visits without disturbing other teachers. By establishing a prioritized requirement specification based on MoSCoW method, which is a result of interviews and survey responses from teachers, the requirements are identified for a system for the positioning of teachers and related systems for visitor management.Based on Pahl's development process for the preparation of a technical artifact, some different solutions for indoor positioning is analyzed and evaluated, leading to a proposal regarding a prototype. The prototype relates to symbolic positioning in a system for proximity detection by predefined zones, based on active mobile phones and passive Bluetooth beacons. Finally, the prototyp for indoor positioning is then implemented in combination with a system for visitor management to demonstrate its feasibility. This in the form of a summed system view that includes a cloud-based database, a web interface and two Android applications.

There is a high demand for Indoor Positioning Systems (IPS) due to the wide application possibilities. The main issue for localization by wireless signals in an indoor environment is the multipath propagation problem. Multipath propagation occurs when signals reflects and refract from an object, changing the signals characteristics. Today there is no IPS that can balance the cost, accuracy, and complexity. In January 2019 Bluetooth released a new standard, Bluetooth Low Energy (BLE) 5.1, which enables the ability to measure the Angle of Arrival (AoA) of an incoming signal. The purpose of this thesis is to mitigate the disturbances caused by multipath propagation by conducting a case study. This was done by designing systems that combine different positioning techniques with sensor fusion and evaluating them based on power efficiency, execution time and precision. Two wireless localization techniques were evaluated, trilateration and triangulation, which are based on Received Signal Strength Indication (RSSI) and AoA respectively, along with an Inertial Measurement Unit (IMU). An Extended Kalman Filter EKF was used to fuse the sensor data. The system with the best overall performance uses the AoA signals and a multipath mitigation technique. The system with AoA and IMU had a similar performance but has an overall higher complexity due to the added IMU component. The RSSI system could not satisfy the requirement of precision. / Det finns en stor efterfrågan på inomhus positionssystem (IPS) tack vare de många möjliga tillämpningarna. Det största problemet för inomhuslokalisering med trådlösa signaler i inomhusmiljö är flervägsutbredningsproblemet. Felvägsutbredning inträffar när signaler reflekterar och bryts på ett objekt, och ändrar dess karaktär. Idag finns det inga IPS som kan balansera kostnaden, noggrannhetoch komplexitet. I januari 2019, släppte Bluetooth en ny standard, Bluetooth Low Energy (BLE) 5.1, som gör det möjligt att mäta ankomstvinkeln (AoA) för en inkommande signal. Syftet med denna avhandling är att minska störningarna orsakade av flervägsutbredning genom att utföra en fallstudie. Detta gjordes genom att designa system som kombinerar olika positioneringstekniker med sensor fusion och utvärdera dessa med avseende på energieffektivitet, exekveringstid och precision. Två trådlösa lokaliseringstekniker utvärderades, trilaterering och triangulering, som är baserade på mottagen signalstyrka indikation RSSI och AoA respektive, tillsammans med en tröghetssensor (IMU). Ett Utökat Kalman-Filter (EKF) användes för att kombinera data. Systemet med den överlag bästa prestandan använder AoA-signaler och en teknik för att dämpa flervägsutredningen. Systemet med AoA och IMU hade liknande prestanda men den totala komplexiteten ökade med att lägga till en IMU komponent. RSSI systemet var inte tillräckligt tillfredsställande med avseende på precisionskraven.