Whilst GPS positioning has been a well used technology for many years in outdoor environments,a ubiquitous solution for indoor positioning is yet to be found, as GPS positioning is unreliableindoors. This thesis focuses on the combination of Inertial Sensor Dead Reckoning and positionsobtained from the Bluetooth Low Energy (BLE) Direction Finding technique. The main objectiveis to reduce the error rate and size of a BLE Direction Finding system. The positioned object is aMicro-Electrical Mechanical System (MEMS) with an accelerometer and a gyroscope, placed on atrolley. The accelerometer and gyroscope are used to obtain an orientation, velocity vector, andin turn a position which is combined with the BLE Direction Finding position. To further reducethe error rate of the system, a Stationary Detection functionality is implemented. Because of thetrolley movement pattern causing noise in the sensor signals, and the limited sensor setup, it is notpossible to increase the accuracy of the system using the proposed method. However, the StationaryDetection is able to correctly determine a stationary state and thus decreasing error rate and powerconsumption. / GPS är en väl använd teknologi sedan många år, men på grund av dess bristande precision vid inomhuspositionering, behöver en ny teknologi för detta område hittas. Denna studie är fokuserad på Dead Reckoning som ett stöd till ett Bluetooth Direction Finding positioneringssystem. Det främsta målet är att minska felfrekvensen och felstorleken i BLE Direction Finding systemet. Föremålet som positioneras är en Micro-Electrical Mechanical System (MEMS) med en accelerometer och ett gyroskop, placerad på en vagn. Accelerometern och gyroskopet används för att erhålla en orientering, hastighetsvektor och därefter en position som kombineras med den position som ges av BLE Direction Finding. För att minska felfrekvensen ytterligare hos systemet, implementeras en funktionalitet som detekterar om MEMS-enheten är stillastående, kallad Stationary Detection. På grund av vagnens rörelsemönster, som bidrar till brus hos sensorsignalerna, samt den begränsade sensorkonfigurationen, är det inte möjligt att förbättra systemets precision med den föreslagna metoden. Dock kan Stationary Detection korrekt fastställa ett stationärt tillstånd och därmed minska felfrekvensen och energiförbrukningen för enheten.

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This thesis investigates applicable indoor navigation systems for the next generation of hand-held shopping scanners, on behalf of the company Virtual Stores. The thesis research and review applicable indoor localization methods and ways to combine and evaluate received localization data in order to provide accurate navigation without introducing any other worn equipment for a potential user. Prototype navigation systems was proposed, developed and evaluated using a combination of carefully placed radio transmitters that was used to provide radio based localization methods using Bluetooth or UltraWide Band (UWB) and inertial sensors combined with a particle filter. The Bluetooth solution was deemed incapable of providing any accurate localization method while the prototype using a combination of UWB and inertial sensors proved promising solution with below 1m average error under optimal conditions or 2.0m average localization error in a more realistic environment. However, the system requires the surveyed area to provide 3 or more UWB transmitters in the line of sight of the UWB receiver of the user at every location facing any direction to provide accurate localization. The prototype also requires to be scaled up to provide localization to more than 1 radio transmitters at the time before being introduced to the Fast moving consumer goods market. / Denna avhandling undersöker tillämpliga inomhusnavigationssystem för nästa generations handhållna shopping terminaler, på uppdrag av företaget Virtual Stores. Avhandlingen undersöker och granskar tillämpliga inomhuslokaliseringsmetoder och sätt att kombinera och utvärdera mottagna lokaliseringsdata för att bistå med ackurat navigering utan att introducera någon ytterligare utrustning för en potentiell användare. Prototypnavigationssystem föreslogs, utvecklades och utvärderades användandes en kombination av väl utplacerade radiosändare användandes Bluetooth eller UltraWide Band (UWB) och tröghetssensorer i kombination med ett partikelfilter. Bluetooth-lösningen ansågs oförmögen att tillhandahålla någon exakt lokalisering medan prototypen som använde en kombination av UWB och tröghetssensorer visade sig vara en lovande lösnings med under 1m genomsnittligt fel under optimala förhållanden eller 2,0m genomsnittligt lokaliseringsfel i mer realistisk miljö. Systemet kräver emellertid att det undersökta området tillhandahåller 3 eller fler UWB-sändare inom synfältet för UWB-mottagaren hos användaren vid varje plats och riktning för att tillhandahålla ackurat lokalisering. Prototypen behöver skalas upp för att kunna bistå med lokalisering till mer än 1 radiomottagare innan den introduceras till detaljhandlen.

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Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such as Wireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework. / Ph. D.

Due to the recursive and integrative nature of zero-velocity-update-aided (ZUPT-aided) inertial navigation systems (INSs), the error covariance increases throughout each ZUPT-less period followed by a drastic decrease and large state estimate corrections as soon as ZUPTs are applied. For dead-reckoning with foot-mounted inertial sensors, this gives undesirable discontinuities in the estimated trajectory at the end of each step. However, for many applications, some degree of lag can be tolerated and the information provided by the ZUPTs at the end of a step can be made available throughout the step, eliminating the discontinuities. For this purpose, we propose a smoothing algorithm for ZUPT-aided INSs. For near real-time applications, smoothing is applied to the data in a step-wise manner requiring a suggested varying-lag segmentation rule. For complete off-line processing, full data set smoothing is examined. Finally, the consequences and impact of smoothing are analyzed and quantified based on real-data. / <p>QC 20130114</p>

Les travaux décrits dans cette thèse s'articulent autour des architectures de communication en réseaux locaux et réseaux distants pour les applications de simul! ation distribuée interactive, particulièrement dans le cadre d! u projet Platsim. Nous avons traité dans un premier temps, les aspects gestion de la QoS pour les simulations distribuées basées sur les middlewares HLA et DDS en réseaux locaux, et ensuite nous avons étendu cette contribution avec DDS sur des réseaux grandes distances. La première contribution consiste à enrichir PlatSim par un modèle formel pour la gestion de la QoS que nous avons implémentée sur HLA pour combler les manques de QoS dont souffre ce middleware. Ensuite, nous avons proposé une architecture pour l'interconnexion des simulateurs distribués avec le middleware DDS. L'utilisation de DDS est intéressante pour la simplicité de son implémentation et ses performances de communication déjà prouvées sur des systèmes complexes. Dans la deuxième contribution, nous avons développé un algorithme de navigation à l'estime (dead-reckoning) pour l'anticipation du comportement des entités simulées. Cette approche permet d'émuler leur comportement lors de la déterm! ination de l'erreur maximale admissible satisfaisant les contraintes de la QoS requise, ce qui, en cas de défaillance du système de communication, permet d'estimer le comportement des objets simulés. Ensuite, nous avons présenté une proposition pour l'interconnexion des simulations distribuées DDS et cette approche de dead-reckoning, par deux mécanismes différents: dans un premier temps, nous avons montré qu'il est possible d'utiliser le service de routage DDS pour mettre en place un "pont-fédéré" DDS permettant d'interconnecter des domaines DDS différents dans un même domaine IP, et ensuite nous avons proposé un "Proxy DDS" qui permet d'interconnecter des simulations DDS situées dans des domaines DDS différents et des domaines IP hétérogènes. Enfin, nos deux dernières contributions con cernent l'étude et la mise en place d'une architecture de communication à grande distance à QoS garantie pour les simulations distribuées sur DDS. Tout d'abord, nous avons présenté! une architecture de signalisation de la QoS pour en se basant! sur l'u tilisation conjointe du protocole COPS et de la signalisation SIP. Ensuite, nous avons étendu des travaux réalisés au LAAS-CNRS dans le cadre du projet européen EuQoS. Nous avons alors utilisé des composants de cette architecture que nous avons adaptés pour fournir, à l'utilisateur final ou à l'administrateur de l'application, des interfaces simples lui permettant de demander le type de service requis pour son application sans avoir besoin de changer le protocole de signalisation.

This thesis is a feasibility study of contemporary indoor positioning approaches in an hospital environment using sensor available on Android phones together with Wi-Fi fingerprintingand map information. The purpose is to determine the resolution of pedestrian indoor positioning and whether it is sufficient for room level accuracy. Accurate and robust positioning for outdoor applications based on mobile networks and satellite systems, such as the Global Positioning Service (GPS), has been around for many years. However these systems are not suitable for positioning inside buildings due to a high level of signal degradation. Through the years various pedestrian indoor positioning methods have been proposed.A simple algorithm for suppressing random movement of the mobile phone is tested. Two versions of the Extended Kalman Filter (EKF) are compared for fusing the Inertial Navigation System (INS) measurements during Pedestrian Dead Reckoning (PDR). The TRIAD algorithm is tested for suppressing the effects of large magnetic disturbances. Wi-Fi fingerprinting using two combinations of positioning algorithms and radio maps is tested. The EKF is tested for fusing PDR and Wi-Fi fingerprint position estimations. The Particle Filter (PF) is tested for combining PDR with Wi-Fi fingerprint positioning with a geometrical map. Static Received Signal Strength Indication (RSSI) measurements are carried out to detect variable Wi-Fi transmission power. The results show that adding more informations sources improves the positioning performance. Also fusion using PF outperforms the EKF in more complex indoor environments and movement patterns. / En starkt växande tillgång och kapacitet hos trådlösa nätverk i kombination med explosionen inom mobiltelefoni, i synnerhet vad gäller smartphones, har lett till ett enormt ökat intresse för och utveckling inom området inomhuspositionering. Det har under många år funnits lösningar för positionering i utomhusmiljöer, exempelvis GPS och triangulering med mobila basstationer, men inga av dessa system lämpar sig för inomhuspositionering eftersom signalerna tappar alldeles för mycket i intensitet när användaren befinner sig inomhus. Under årens lopp har flera olika lösningar för inomhuspositionering föreslagits. I denna uppsats testas olika lösningar för inomhuspositionering med smartphones i en sjukhusmiljö. Testen baserar sig på de sensorer som finns i en smartphone med operativsystemet Android i kombination med Wi-Fi triangulering och en digital planlösning över testområdet. Syftet är att undersöka om noggrannheten kan bli såpass så bra att en upplösning på rumsnivå uppnås. En enkel algoritm för att kompensera för slumpartade och oplanerade rörelser hos mobiltelefonen testas.  Två versioner av det utökade Kalmanfiltret testas för tröghetsnavigering. TRIAD algoritmen testas för att motverka magnetiska störningar. Två kombinationer av radiokartor och positioneringsalgoritmer provas för att genomföra Wi-Fi positionering. Ett utökat Kalmanfilter används för att kombinera resultaten av tröghetsnavigeringen med Wi-Fi positioneringen. Ett partikelfilter används för att utföra sensorfusionen av tröghetsnavigeringen, Wi-Fi positioneringen och den digitala planlösningen. Resultaten visar att ju mer information som tillförs under positioneringen desto större blir noggrannheten samt att partikelfiltret ger en bättre noggrannhet i en komplex inomhusmiljö i kombination med komplicerade rörelsemönster än det utökade Kalmanfiltret.

Aim of this thesis is to search the market for suitable autonomous robot to be used by smart house. The research in this work is partly done on the range of abilities of smart houses in matter of sensor systems, ability of data processing and their use by mobile robots. The output of this thesis is robotics application written using Microsoft Robotics Developer Studio (C#) and simulated using Visual Simulation Environment. Main feature of this robotic application is the interface between robot and smart house, and robot and user. This interface enables employer to directly control robot's movement or to use automated pathfinding. The robot is able to navigate in dynamic environment and to register, interact and eventually forget temporary obstacles.

The aim of this thesis is to write a multisensor navigation algorithm and to design a test setup. After doing these, test the algorithm by using the test setup. In navigation, dead reckoning is a procedure to calculate the position from initial position with some measured inputs. These measurements do not include absolute position data. Using only an inertial measurement unit is an example for dead reckoning navigation. Calculating position and velocity with the inertial measurement unit is highly erroneous because, this calculation requires integration of acceleration data. Integration means accumulation of errors as time goes. For example, a constant acceleration error of 0.1 m/s^2 on 1 m/s^2 of acceleration will lead to 10% of position error in only 5 seconds. In addition to this, wrong calculation of attitude is going to blow the accumulated position errors. However, solving the navigation equations while knowing the initial position and the IMU readings is possible, the IMU is not used solely in practice. In literature, there are studies about this topic and in these studies / some other sensors aid the navigation calculations. The aiding or fusion of sensors is accomplished via Kalman filter. In this thesis, a navigation algorithm and a sensor fusion algorithm were written. The sensor fusion algorithm is based on estimation of IMU errors by use of a Kalman filter. The design of Kalman filter is possible after deriving the mathematical model of error propagation of mechanization equations. For the sensor fusion, an IMU, two incremental encoders and a digital compass were utilized. The digital compass outputs the orientation data directly (without integration). In order to find the position, encoder data is calculated in dead reckoning sense. The sensor triplet aids the IMU which calculates position data by integrations. In order to mount these four sensors, an unmanned tracked vehicle prototype was manufactured. For data acquisition, an xPC&ndash / Target system was set. After planning the test procedure, the tests were performed. In the tests, different paths for different sensor fusion algorithms were experimented. The results were recorded in a computer and a number of figures were plotted in order to analyze the results. The results illustrate the benefit of sensor fusion and how much feedback sensor fusion is better than feed forward sensor fusion.