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. / Sensor Fusion is an essential paradigm in many Engineering fields. Information from different nodes, sensing various phenomena, is integrated to produce a general synthesis of the individual data. Sensor fusion provides a better understanding of the sensed phenomenon, improves the application or system performance, and helps overcome noise in individual measurements. In this dissertation we study some sensor fusion applications in wireless communications: (i) cooperative modulation classification and (ii) indoor localization and mapping at different levels. In cooperative modulation classification, data from different wireless distributed nodes is combined to generate a decision about the modulation scheme of an unknown wireless signal. For indoor localization and mapping, measurement data from smartphone sensors are combined through Pedestrian Dead Reckoning (PDR) to re-create movement trajectories of indoor mobile users, thus providing high-accuracy estimates of user’s locations. In addition, measurements from collaborating users inside buildings are combined to enhance the trajectories’ estimates and overcome limitations in single users’ system performance. The results presented in both parts of this dissertation in different frameworks show that combining data from different collaborative sources greatly enhances systems’ performances, and open the door for new and smart applications of sensor fusion in various wireless communications areas.

Force est de constater aujourd’hui que la localisation d’un bien ou d’une personne est devenue une nécessité. Plusieurs solutions existent en extérieur, largement dominées par le système GPS. Pour la localisation en intérieur, la précision se dégrade en raison des trajets multiples et de l’atténuation des signaux traversant les murs. Cette thèse se focalise sur la problématique de localisation à l’intérieur d’un bâtiment en utilisant les technologies présentes dans des smartphones et des tablettes fonctionnant sous le système d’exploitation Android disponible dans divers marques. Les systèmes de localisation en intérieur exploitent différents supports tels que les ondes radio-fréquence (RF) ou les capteurs inertiels embarqués dans un terminal. Dans le cas RF, ils utilisent des points références dont la répartition sur la zone couverte influe sur la performance en localisation. Une première contribution est un développement d’algorithme d’optimisation d’emplacement des balises basé sur le recuit simulé. Les signaux extraits des capteurs inertiels sont utilisés par la navigation pédestre à l’estime (NPE) pour déterminer le trajet effectué depuis une position connue. Ils dépendent de la sensibilité des paramètres intrinsèques de ces capteurs et ils sont corrompus par des bruits. Dans le cas NPE, une calibration permet d’obtenir des données exploitables pour l’estimation de l’orientation de déplacement et pour la détection des pas. Cette orientation est supposée identique à celle du terminal mais il y a un intérêt à prendre en compte le biais d’orientation entre les deux. Une autre contribution est une proposition d’algorithme de détection des pas exploitant la logique floue. / Nowadays, the localization of a device or person has become mandatory. If many solutions exist for outdoor environment, as the GPS one, any fails to provide an expected accuracy for indoor environment because of the multipath phenomena and the attenuation of signals crossing walls. This thesis focuses on the localization problem in buildings by using existed technologies in smartphones and tablets managed by Android OS - which is available in several brands. The indoor localization systems are using different technologies like radio-frequency (RF) waves or inertial sensors embedded in handsets. In the RF case, they use anchors or beacons, whose position impacts the localization performance for the covered zone. Our first contribution was the placement optimization of beacons using simulated annealing algorithm. Next to improve the localization performance, the inertial sensors, embedded in smartphones, have been used. The pedestrian dead reckoning (PDR) algorithm employs the extracted signals from the inertial sensors and determines the path done since a known position. These extracted signals are affected by the intrinsic parameters of sensors and they are corrupted by noises. The calibration of the sensors is compulsory to obtain data that could be used to estimate the walking orientation and the number of done steps by the user. It is often supposed that the walking orientation is the same as the smartphone orientation; however it might be interesting to consider the bias between these two orientations. A last contribution, in this thesis, consists on a proposed algorithm for step detection using fuzzy logic.

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.

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.

Denna rapport sammanfattar resultat av ett examensarbete på en högskoleingenjörsutbildningsom utfördes av två studenter på Malmö högskola.Arbetets syfte var att genomföra en mängd olika experiment med accelerometer, barometeroch GPS i en modern smarttelefon. Ett antal scenarier för en tänkbar sensoranvändning i applikationerformulerades för att vägleda olika experiment. Experimentdata dokumenterades noggrantoch analyserades med avsikten att skapa en databank med information för framtida studier.Analys av data inkluderar höjdbestämning i naturen och i byggnader med hjälp av barometersensor,geografisk position med hjälp av GPS, hastighet och acceleration under en hissfärdmed hjälp av accelerometer.Rapporten innehåller en omfattande litteraturstudie om användning av sensorer vid inomhuspositionering.Från analys av mätdata, kom vi fram till slutsatsen att är möjligt att beräkna höjdenfrån barometerdata med bra noggrannhet under optimala omständigheter. GPS höjdenfrån mätningarna har stor felmarginal jämfört med den verkliga höjden samt när den jämförsmed den beräknade höjden från barometern.Genom att utföra en numerisk integration på accelerometer-data kom vi fram till att det är möjligtatt beräkna ungefär hur långt man har färdats med en hiss, dock att vissa detaljer måste tasi beaktning. / This report summarizes the results of a degree Bachelor of engineering in Computer Scienceconducted by two students at Malmo University.Work aim was to conduct a variety of experiments with accelerometer, barometer and GPS in amodern smartphone. A number of scenarios for a possible sensor use in applications formulatedto guided experiments. Data is carefully documented and analyzed, with the intention tocreate a database of information for future studies. Analysis of the data includes the altitudedetermination in nature and in buildings using barometric sensor, geographic location usingGPS, speed and acceleration during an elevator journey with the help of accelerometer.The report contains a comprehensive literature review on the use of sensors for indoorpositioning.From our analysis of the measurement data, we conclude that it is possible to calculate thealtitude from barometric- information but good accuracy if there are optimum circumstances.GPS altitude from our measurements show faulty height by a large margin compared with theactual height and when it is compared with the calculated height of the barometer the barometricheight is closer to the actual height.By performing a numerical integration of the accelerometer data, the results show that it ispossible to calculate approximately how far you have traveled in meters in an elevator, howeverthere are some things that must be taken into consideration.