الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigmfor integrating data from multiple sources to synthesize new information that in general would nothave been feasible from the individual parts. Within the wireless communications fields, manyemerging technologies such asWireless Sensor Networks (WSN), the Internet of Things (IoT), andspectrum sharing schemes, depend on large numbers of distributed nodes working collaborativelyand sharing information. In addition, there is a huge proliferation of smartphones in the world witha growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitora 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 ofsensor fusion techniques at multiple levels.In this dissertation, we identify two key problems in wireless communications that can greatlybenefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoorlocalization and mapping based on smartphone sensors. Automatic Modulation Classification isa key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications.Although extensively researched, performance of signal classification at a single nodeis largely bounded by channel conditions which can easily be unreliable. Applying sensor fusiontechniques to the signal classification problem within a network of distributed nodes is presentedas a means to overcome the detrimental channel effects faced by single nodes and provide morereliable classification performance.Indoor localization and mapping has gained increasing interest in recent years. Currently deployedpositioning techniques, such as the widely successful Global Positioning System (GPS), are optimizedfor outdoor operation. Providing indoor location estimates with high accuracy up to theroom or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometersand gyroscopes, provided attractive solutions to the indoor localization problem throughPedestrian 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 localizationproblem at two different levels: fusion of measurements from different sensors in asmartphone, and fusion of measurements from several smartphones within a collaborative framework.