GPS has become very popular in recent years. It is used in wide range of applications including aircraft navigation, search and rescue, space borne attitude and position determination and cellular network synchronization. Each application places demands on GPS for various levels of accuracy, integrity, system availability and continuity of service. Radio frequency interference (RFI) which results from many sources such as TV/FM harmonics, radar or mobile satellite systems, presents a challenge to the use of GPS. It can affect all the service performance indices mentioned above. To improve the accuracy of GPS positioning, a continuously operating reference station (CORS) network can be used. A CORS network provides all the enabled GPS users in an area with corrections to the fundamental measurements, producing more precise positioning. A threat to these networks is a threat to all high-accuracy GPS users. It is therefore necessary to monitor the quality of the received signal with the objective of promptly detecting the presence of RFI and providing a timely warning of the degradation of system accuracy, thereby boosting the integrity of GPS. This research was focused on four main tasks: a) Detection. The focus here is on a power spectral density fluctuation detection technique, in which statistical inference is used to detect narrowband continuous-wave (CW) interference in the GPS signal band after being captured by the RF front-end. An optimal detector algorithm is proposed. At this optimal point, for a fixed Detection Threshold (DT), probability of false alarm becomes minimal and for a fixed probability of false alarm, we can achieve the minimum value for the detection threshold. Experiments show that at this point we have the minimum computational load. This theoretical result is supported by real experiments. Finally this algorithm is employed to detect a real GPS interference signal generated by a TV transmitter in Sydney. b) Characterization. In the characterization section, using the GNSS signal structure and the baseband signal processing inside the GNSS receiver, a closed formula is derived for the received signal quality in terms of effective carrier to noise ratio ( ). This formula is tested and proved by calculating the C/No using the I and Q data from a software GPS receiver. For pulsed CW, a similar analysis is done to characterize the effect of parameters such as pulse repetition period (PRP) and also duty cycle on the received signal quality. Considering this characterization and the commonality between the GPS C/A code and Galileo signal as a basis to build up a common term for satellite availability, the probability of satellite availability in the presence of CW interference is defined and for the two currently available satellite navigation systems (GPS L1 signal and Galileo signal (GIOVE-A BOC(1, 1) in the E1/L1 band)) it is shown that they can be considered as alternatives to each other in the presence of different RFI frequencies as their availability in the presence of CW RFI is different in terms of RFI frequency. c) Mitigation. The last section of the research presents a new concept of ?Satellite Exclusion Zone?. In this technique, using our previously developed characterization techniques, and considering the fact that RFI has different effects on different satellite signals at different times depending on satellite Doppler frequency, the idea of excluding the most vulnerable satellite signal from positioning calculations is proposed. Using real data and real interference, the effectiveness of this technique is proven and its performance analyzed. d) Hardware implementation. The above detection technique is implemented using the UNSW FPGA receiver board called NAMURU.

Modern civil structures as well as loads on them are still too complex to be accurately modeled or simulated. Therefore, structural failures and structural defects are NOT uncommon! More and more full-scale structural monitoring systems have been deployed in order to monitor how structures behave under various loading conditions. This research focuses on how to maximise benefits from such full-scale measurements by employing advanced digital signal processing techniques. This study is based on accelerometer and GPS data collected on three very different structures, namely, the steel tower in Tokyo, the long and slender suspension bridge in Hong Kong, and the tall office tower in Sydney, under a range of loading conditions, i.e., typhoon, earthquake, heavy traffic, and small scale wind. Systematic analysis of accelerometer and GPS data has demonstrated that the two sensors complement each other in monitoring the static, quasi-static and dynamic movements of the structures. It has also been confirmed that the Finite Element Model could under-estimate the natural frequencies of structures by more than 40% in some case. The effectiveness of using wavelet to de-noise GPS measurement has been demonstrated. The weakness and strengths of accelerometer and GPS have been identified and framework has been developed on how to integrate the two as well as how to optimize the integration. The three-dimensional spectral analysis framework has been developed which can track the temporal evolution of all the frequency components and effectively represents the result in the 3D spectrogram of frequency, time and magnitude. The dominant frequency can also be tracked on the 3D mesh to vividly illustrate the damping signature of the structure. The frequency domain coherent analysis based on this 3D analysis framework can further enhance the detection of common signals between sensors. The developed framework can significantly improve the visualized performance of the integrated system without increasing hardware costs. Indoor experiments have shown the excellent characteristics of the optical fibre Bragg gratings (FBGs) for deformation monitoring. Innovative and low-cost approach has been developed to measure the shift of FBG???s central wavelength. Furthermore, a schematic design has been completed to multiplex FBGs in order to enable distributed monitoring. In collaboration with the University of Sydney, the first Australian full-scale structural monitoring system of GPS and accelerometer has been deployed on the Latitude Tower in Sydney to support current and future research.

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This thesis describes a blind beamforming technique for GPS receivers. It improves the performance of a GPS receiver by mitigating interference and enhancing GPS signals separately and has a three-stage structure. The technique is based on a linear antenna array and integrates the eigendecomposition based subspace and multiple independent beamforming techniques. A signal model is carefully constructed. Particular emphasis is placed upon the projection matrix derived from the subspace technique. The effect of interference and phase error on this technique is discussed. This technique is tested and compared to null steering and MMSE technique using simulated data for a number of interference environments. Furthermore, the proposed technique is applied to real data and shows several advantages over simple null steering. / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references.

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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.