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Vehicle self-localization, the ability of a vehicle to determine its own location, is vital for many aspects of Intelligent Transportation Systems (ITS) and telematics where it is often a building block in a more complex system. Navigation systems are perhaps the most obvious example, requiring knowledge of the vehicle's location on a map to calculate a route to a desired destination. Other pervasive examples are the monitoring of vehicle fleets for tracking shipments or dispatching emergency vehicles, and in public transit systems to inform riders of time-of-arrival thereby assisting trip planning. These system often depend on Global Positioning System (GPS) technology to provide vehicle localization information; however, GPS is challenged in urban environments where satellite visibility and multipath conditions are common. Vehicle localization is made more robust to these issues through augmentation of GPS-based localization with complementary sensors, thereby improving the performance and reliability of systems that depend on localization information. This thesis investigates the augmentation of vehicle localization systems with visual context. Positioning the vehicle with respect to objects in its surrounding environment in addition to using GPS constraints the possible vehicle locations, to provide improved localization accuracy compared to a system relying solely on GPS. A modular system architecture based on Bayesian filtering is proposed in this thesis that enables existing localization systems to be augmented by visual context while maintaining their existing capabilities. It is shown in this thesis that localization errors caused by GPS signal multipath can be reduced by positioning the vehicle with respect to visually-detected intersection road markings. This error reduction is achieved when the identities of the detected road marking and the road being driven are known a priori. It is further shown how to generalize the approach to the situation when the identities of these parameters are unknown. In this situation, it is found that the addition of visual context to the vehicle localization system reduces the ambiguity of identifying the road being driven by the vehicle. The fact that knowledge of the road being driven is required by many applications of vehicle localization makes this a significant finding. A related problem is also explored in this thesis: that of using vehicle position information to augment machine vision. An approach is proposed whereby a machine vision system and a vehicle localization system can share their information with one another for mutual benefit. It is shown that, using this approach, the most uncertain of these systems benefits the most by this sharing of information. Augmenting vehicle localization with visual context is neither farfetched nor impractical given the technology available in today's vehicles. It is not uncommon for a vehicle today to come equipped with a GPS-based navigation system, and cameras for lane departure detection and parking assistance. The research in this thesis brings the capability for these existing systems to work together.

Vehicle self-localization, the ability of a vehicle to determine its own location, is vital for many aspects of Intelligent Transportation Systems (ITS) and telematics where it is often a building block in a more complex system. Navigation systems are perhaps the most obvious example, requiring knowledge of the vehicle's location on a map to calculate a route to a desired destination. Other pervasive examples are the monitoring of vehicle fleets for tracking shipments or dispatching emergency vehicles, and in public transit systems to inform riders of time-of-arrival thereby assisting trip planning. These system often depend on Global Positioning System (GPS) technology to provide vehicle localization information; however, GPS is challenged in urban environments where satellite visibility and multipath conditions are common. Vehicle localization is made more robust to these issues through augmentation of GPS-based localization with complementary sensors, thereby improving the performance and reliability of systems that depend on localization information. This thesis investigates the augmentation of vehicle localization systems with visual context. Positioning the vehicle with respect to objects in its surrounding environment in addition to using GPS constraints the possible vehicle locations, to provide improved localization accuracy compared to a system relying solely on GPS. A modular system architecture based on Bayesian filtering is proposed in this thesis that enables existing localization systems to be augmented by visual context while maintaining their existing capabilities. It is shown in this thesis that localization errors caused by GPS signal multipath can be reduced by positioning the vehicle with respect to visually-detected intersection road markings. This error reduction is achieved when the identities of the detected road marking and the road being driven are known a priori. It is further shown how to generalize the approach to the situation when the identities of these parameters are unknown. In this situation, it is found that the addition of visual context to the vehicle localization system reduces the ambiguity of identifying the road being driven by the vehicle. The fact that knowledge of the road being driven is required by many applications of vehicle localization makes this a significant finding. A related problem is also explored in this thesis: that of using vehicle position information to augment machine vision. An approach is proposed whereby a machine vision system and a vehicle localization system can share their information with one another for mutual benefit. It is shown that, using this approach, the most uncertain of these systems benefits the most by this sharing of information. Augmenting vehicle localization with visual context is neither farfetched nor impractical given the technology available in today's vehicles. It is not uncommon for a vehicle today to come equipped with a GPS-based navigation system, and cameras for lane departure detection and parking assistance. The research in this thesis brings the capability for these existing systems to work together.

Low speed urban streets are designed to provide both access and mobility, and accommodate multiple road users, such as bicyclists and pedestrians. However, speeds on these facilities often exceed the intended operating speeds as well as their design speeds. Several studies have indicated that the design speed concept, as implemented in the roadway design process in the United States, does not guarantee a consistent alignment that promotes uniform operating speeds less than design speeds. To overcome these apparent shortfalls of the design speed approach, a promising design approach is a performance-based design procedure with the incorporation of operating speeds. Under this procedure, the geometric parameters of the roadways are selected based on their influences on the desired operating speeds. However, this approach requires a clear understanding of the relationships between operating speeds and various road environments. Although numerous studies have developed operating speed models, most of these previous studies have concentrated on high speed rural two-lane highways. In contrast, highway designers and planners have very little information regarding the influence of low speed urban street environments on drivers' speeds. This dissertation investigated the relationship between drivers' speed choices and their associated low speed urban roadway environments by analyzing second-by-second in-vehicle GPS data from over 200 randomly selected vehicles in the Atlanta, Georgia area. The author developed operating speed models for low speed urban street segments based on roadway alignment, cross-section characteristics, roadside features, and adjacent land uses. The author found the number of lanes per direction of travel had the most significant influence on drivers' speeds on urban streets. Other significant variables include on-street parking, sidewalk presence, roadside object density and offset, T-intersection and driveway density, raised curb, and adjacent land use. The results of this research effort can help highway designers and planners better understand expected operating speeds when they design and evaluate low speed urban roadways.

Taipower Company has already completed the installation of the Outage Management System (OMS) in all service districts. The attributes of all distribution equipments and customers have been included in the computerized mapping system. However, the phasing attributes of distribution transformers are not very accurate in the database because they are very difficult to be identified for the distribution systems. The phase information of transformers in the OMS database is often inconsistent with the actual service phase, which deteriorates the performance of distribution system analysis, planning, and operation of Taipower distribution systems. The objective of this thesis is to develop an innovative Phasor Measurement Unit (PMU) to support the phasing identification of distribution transformers in a very effective way. The proposed PMU is used to measure the low voltage signal at the secondary side of transformers to prevent the survey personnel from safety problem. With the measured phases information of distribution transformers stored in the embedded system, the attributes of transformer phases in OMS can be updated to improve the accuracy of database. For the underground distribution systems, it is very difficult to receive the 1PPS signal of GPS system for timing synchronous to support the proposed transformer phasing measurement because all transformers are located at the building basement. This thesis proposes a timing synchronous module by using the Temperature Compensated Crystal Oscillator, TCXO to maintain the timing accuracy with high precision. Before executing the phasing measurement, this module is calibrated using the GPS 1PPS signal with fuzzy control calibration algorithm. It is found that the proposed PMU module can maintain the timing synchronous with 6˚, during two hours time period which will support the transformer phasing measurement by providing the reference timing synchronous even without the GPS 1PPS signal.

GPS is a widely accepted means of navigation, whether it is for civilian or military means. With the implementation of GPS on smart projectiles, these weapons have been able to achieve remarkable accuracy. Even though the improvements in accuracy are impressive, GPS signals are susceptible to jamming and spoofing by a sufficiently motivated enemy. The work reported here examines the viability of constructing a navigation solution using ground based signals of opportunity that provide range and range rate information. Using a generalized sensor model encompassing the key error terms, a variety of physical devices are included in the analysis. For a typical indirect fire trajectory, navigation solutions are computed as a function of the number and density of signal sources, terrain type, and sensor errors. Systematic studies were performed using these parameters in order to better understand the merits and demerits of this type of system to create a useful navigation solution. Based on these studies, results indicate that navigation solutions can be computed with the same accuracy as current GPS systems with a moderate number of signal sources. Generally, more accurate solutions are obtained when the projectile is directly over the signal sources and there is variation of signal source location in all three axes.

Advances in sensing and positioning technology, fueled by wide deployment of wireless networks, have made many devices location-aware. These emerging technologies have enabled a new class of applications, known as Location-Based Services (LBS), offering both new business opportunities and a wide array of new quality of life enhancing services. One example of such services is spatial alarms, an enabling technology for location-based advertisement, location-based alerts or reminders and a host of other applications. On the other hand, the ability to locate mobile users accurately also opens door for new threats - the intrusion of location privacy. The time series of location data can be linked to personal identity, which leads to unauthorized information exposure about the individual's medical conditions, alternative lifestyles, unpopular political views or location-based spam and stalking. Thus, there are two important challenges for location-based service provisioning. How do we scale LBSs in the presence of client mobility and location dependent constraints for the multitude of new, upcoming location-based applications under a common framework? How do we provide anonymous location- based services with acceptable performance and quantifiable privacy protection in the next generation of mobile networks, systems and applications? This dissertation delivers technical solutions to address these important challenges. First, we introduce spatial alarms as the basic primitive to represent a class of locationbased services that require location-based trigger capability. Similar to time-based alarms, spatial alarms serve as spatial event reminders that enable us to express different location-based information needs supported by a variety of applications ranging from location-based advertisements, location-based personal assistants, to friend locator services like Google Latitude. We develop a generalized framework and a suite of optimization techniques for server-centric scalable processing of spatial alarms. Our architecture and algorithm development provide significant performance enhancement in terms of system scalability compared to naive spatial alarm processing techniques, while maintaining high accuracy for spatial alarm processing on the server side and reduced communication costs and energy consumption on the client side. Concretely, we develop safe period optimizations for alarm processing and introduce spatial alarm grouping techniques to further reduce the unnecessary safe period computation costs. In addition, we introduce a distributed alarm processing architecture that advocates the partitioning of the alarm processing load among the server and the relevant mobile clients to reduce the server load and minimize the client-to-server communication cost through intelligent distribution and parallelization. We also explore a variety of optimization opportunities such as incorporating non-spatial constraints into the location-based information monitoring problem and utilizing efficient indexing methods such as bitmap indexing to further enhance the performance and scalability of spatial alarm processing in the presence of mobility hotspots and skewed spatial alarm distributions. Second, we develop the PrivacyGrid framework for privacy-enhanced location service provisioning, focusing on providing customizable and personalized location privacy solutions while scaling the mobile systems and services to a large number of mobile users and a large number of service requests. The PrivacyGrid approach has three unique characteristics. First, we develop a three-tier architecture for scaling anonymous information delivery in a mobile environment while preserving customizable location privacy. Second, we develop a suite of fast, dynamic location cloaking algorithms. It is known that incorporation of privacy protection measures may lead to an inherent conflict between the level of privacy and the quality of services (QoS) provided by the location-based services. Our location cloaking algorithms can scale to higher levels of location anonymity while achieving a good balance between location privacy and QoS. Last but not the least; we develop two types of location anonymization models under the PrivacyGrid architecture, one provides the random way point mobility model based location cloaking solution, and the other provides a road network-based location privacy model powered by both location k-anonymity and segment s-anonymity. A set of graph-based location cloaking algorithms are developed, under the MobiCloak approach, to provide desired levels of privacy protection for users traveling on a road network through scalable processing of anonymous location services. This dissertation, to the best of our knowledge, is the first one that presents a systematic approach to the design and development of the spatial alarm processing framework and various optimization techniques. The concept of spatial alarms and the scaling techniques developed in this dissertation can serve as building blocks for many existing and emerging location-based and presence based information and computing services and applications. The second unique contribution made in this dissertation is its development of the PrivacyGrid architecture for scaling anonymous location based services under the random waypoint mobility model and its extension of the PrivacyGrid architecture through introducing the MobiCloak road-network based location cloaking algorithms with reciprocity support for spatially constrained network mobility model. Another unique feature of the PrivacyGrid and MobiCloak development is its ability to protect location privacy of mobile users while maintaining the end-to-end QoS for location-based service provisioning in the presence of dynamic and personalized privacy constraints.

The advent of Global Positioning System (GPS) has revolutionized geodesy with high accuracy, fast speed, simple use, and low cost. This dissertation investigates three topics on volcano and earthquake-related deformation using GPS measurements and models to demonstrate the power of the new generation of geodetic methods. The three topics include the 2002-2003 continued episodic inflation at Long Valley Caldera in eastern California, the coseismic and postseismic response of the energetic 2008 MW 6.4 Achaia-Elia Earthquake in northwest Peloponnese, Greece, and the interseismic megathrust coupling and forearc sliver transport near the Nicoya Peninsula in northwest Costa Rica.

Diss. (sammanfattning) Umeå : Sveriges lantbruksuniversitet, 2009. / Härtill 4 uppsatser.

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 19, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. C. Mark Cowell. Includes bibliographical references.