Applications of Sensor Fusion to Classification, Localization and Mapping
| dc.contributor.author | Abdelbar, Mahi Othman Helmi Mohamed Helmi Hussein | en |
| dc.contributor.committeechair | Tranter, William H. | en |
| dc.contributor.committeechair | Buehrer, R. Michael | en |
| dc.contributor.committeemember | Roan, Michael J. | en |
| dc.contributor.committeemember | Beex, Aloysius A. | en |
| dc.contributor.committeemember | Elnoubi, Said Mohamed | en |
| dc.contributor.committeemember | Park, Jung-Min | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2018-05-01T08:00:27Z | en |
| dc.date.available | 2018-05-01T08:00:27Z | en |
| dc.date.issued | 2018-04-30 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | 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. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:14558 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/82955 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Sensor Fusion | en |
| dc.subject | Automatic Modulation Classification | en |
| dc.subject | Distributed Networks | en |
| dc.subject | Simultaneous Localization and Mapping | en |
| dc.subject | Indoor Localization | en |
| dc.subject | Pedestrian Dead Reckoning | en |
| dc.title | Applications of Sensor Fusion to Classification, Localization and Mapping | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
Files
Original bundle
1 - 1 of 1