Electronic Version

Based on precise real time orbit and clock products from a global network, the standard Precise Point Positioning (PPP) computes user states, which requires a long initialization time in order to obtain a converged solution. Conversely, the Network Real-Time Kinematic (NRTK) positioning requires additional corrections from regional or local dense network for instantaneous ambiguity resolution and state solutions. Both standard PPP and NRTK modes have their own advantages and limitations.A new approach is developed to generate undifferenced corrections in the observation domain from a regional network which can be disseminated station-by-station and applied to user observations for instantaneous ambiguity-fixing. In the proposed service, instantaneous ambiguity resolution is accessible for regions with these observation corrections as regional augmentation information, so that PPP and RTK are integrated into a unified service.The paper presented the approach for generating the undifferenced corrections and the realization of the augmented system in detail. Results from an operational service are introduced as experimental validation.

Vehicle-to-vehicle (V2V) navigation is reviewed and the concept of differential GPS relative navigation augmented with ultra-wideband (UWB) and bearing measurements is introduced theoretically. Filtering software is developed and tested using a data set collected between three moving vehicles in a test in Calgary. Initial results combining GPS pseudorange, UWB range and bearing measurements show that the additional measurements can significantly improve horizontal positioning accuracy, particularly in environments where GPS availability is poor. The UWB measurements generally contributed to an improved along-track relative position while the bearing measurements improved the across-track position. Whether or not the azimuth of the vehicle making the bearing measurement is known a priori or estimated by the filter is shown to have very little effect on the performance. Data from the three-vehicle test was also used to characterize UWB systematic errors in the V2V environment.

Global Positioning System (GPS) receivers at low latitudes have a high probability of experiencing severe ionospheric scintillations. This paper presents the results of scintillation characteristics and scintillation effect on GPS precise point positioning (PPP), using the data observed by the first ever GNSS scintillation monitoring receiver in Hong Kong. Ionospheric scintillation data were collected in July and August 2012 using a Septentrio PolaRxS Pro receiver located at a station (22°12'N, 114°15'E) in the south of Hong Kong. It was observed that August had much more and stronger scintillations than July in Hong Kong. Amplitude scintillation events ( =0.4) were frequently observed during 21:00-3:00 LT (UT+8 hour) in July and 20:00-4:00 LT in August. Strong scintillations ( =0.8 or ) were mostly observed during 0:00-1:00 LT in July and 20:00-23:00 LT in August. The effect of scintillations on GPS positioning was evaluated using a dual-frequency PPP method. It revealed that under the impact of severe ionospheric scintillations ( =1.0 and =1.0 rad), the largest PPP error can increase to more than 34 cm in the vertical and more than 20 cm in the horizontal components.

A satellite based observation system can continuously or repeatedly generate a user state vector time series that may contain useful information. One typical example is the collection of International GNSS Services (IGS) station daily and weekly combined solutions. Another example is the epoch-by-epoch kinematic position time series of a receiver derived by a GPS real time kinematic (RTK) technique. Although some multivariate analysis techniques have been adopted to assess the noise characteristics of multivariate state time series, statistic testings are limited to univariate time series. After review of frequently used hypotheses test statistics in univariate analysis of GNSS state time series, the paper presents a number of T-squared multivariate analysis statistics for use in the analysis of multivariate GNSS state time series. These T-squared test statistics have taken the correlation between coordinate components into account, which is neglected in univariate analysis. Numerical analysis was conducted with the multi-year time series of an IGS station to schematically demonstrate the results from the multivariate hypothesis testing in comparison with the univariate hypothesis testing results. The results have demonstrated that, in general, the testing for multivariate mean shifts and outliers tends to reject less data samples than the testing for univariate mean shifts and outliers under the same confidence level. It is noted that neither univariate nor multivariate data analysis methods are intended to replace physical analysis. Instead, these should be treated as complementary statistical methods for a prior or posteriori investigations. Physical analysis is necessary subsequently to refine and interpret the results.

With the increasing automation of measurement, adjustment, outlier detection and the consequential use of the results for real-time applications, reliable methods to detect and mitigate an outlier are required. However, it is frequently advised that the outlier test should not be used as a means to automatically reject an outlier. One of the reasons for this is that the outlier detection test at times can identify a wrong measurement. To address this issue, this paper proposes a new outlier separability test to confirm that the identified outlier, by the outlier test, can be confidently rejected as the outlier. In addition, the Minimally Separable Bias and the separability multiplying factor are also obtained for the proposed outlier separability test. With the initial comparisons between the proposed method with the reapplication of the global model test method and multiple hypothesis method, the advantages of the new outlier separability test are demonstrated.

The dynamics of water vapour (WV) has a strong influence on the formation and lifecycle of severe mesoscale convective storm systems due to the large energy transfers in the hydrological processes. Contrary to its importance WV remains poorly understood and inadequately measured both spatially and temporally, especially in the southern hemisphere where meteorological data are sparse. GPS meteorology is currently one of the most important atmospheric remote sensing instruments for meteorology and climatology due to its high spatial and temporal resolutions. Kalman filtering based GPS tomography is a promising method of reconstructing dynamically changing four dimensional (4D) wet refractivity fields. This method takes advantage of the high density and homogeneity of ground-based GPS Continuously Operating Reference Station (CORS) networks. Ground-based GPS tomography has the potential to utilize the dense ground-based infrastructure in Australia providing wet refractivity field solutions at a high spatial and temporal resolution to improve early detection and prediction of severe weather. This research presents a case study based on the analysis of an extreme convective super cell storm in the Victorian region during March 2010 using GPS tomography and the most advanced state-wide CORS network – GPSnet in Australia. Integrated Precipitable Water Vapour (PWV) estimates derived for the MOBS GPS CORS station confirmed high time resolution as well as sensitivity to incoming severe weather. A wet refractivity index adopted for GPS tomographic wet refractivity profiles shows an excessive increase as a response to supercell thunderstorm formation. Finally a 2D cross section mapping over the lifecycle of this severe weather event concludes a correlation between the highly dynamic spatial and temporal changes of wet refractivity modelled using 4D GPS tomography with precipitation intensities measured using weather radars.

Many smart devices like smart phone and tablet nowadays are featured for hybrid sensor platform of GPS chip, inertial sensor(s), magnetic compass and other gadgets such as camera and Wi-Fi. The interest to apply those smart devices for indoor navigation is growing since a large variety of sensors on such devices enable hybrid location solutions to not only improve the availability of indoor positioning but also the accuracy and smoothness. However, in deep indoor scenario, the positioning accuracy is still seldom satisfactory due to large accumulative errors of dead-reckoning sensors. In this paper, a floor plan based vision navigation method is designed for pedestrian handset indoor application. The floor plan for buildings is an easily accessible indoor map with detailed path and room information. It can be matched with the vision measurements from the camera sensor to derive accurate and drift-free positions even in deep indoor environments. The Random Sample Consensus (RANSAC) algorithm is adopted for robust matching between floor plan and camera photo. An iPhone Demo App is developed to evaluate the performance of the designed system and the test results indicate meter-level horizontal accuracy.

Over the past decades, the integration of a MEMS-based (Micro-Electro Mechanical Systems) Inertial Measuring Unit (IMU) with a GNSS receiver-chip has become commonly used navigation techniques by virtue of their advantages such as small sized, light weight, with low power consumption, and have extremely low cost. To provide accurate and reliable positioning solutions with a low-cost GNSS/MEMS INS system, it is valuable to introduce specific auxiliary information that can improve the navigation performance without adding extra hardware costs. The auxiliary information is especially useful during GNSS outage periods or when the vehicle is moving with low dynamics (e.g. no change of attitude and accelerations) which lead to the poor observability of the GNSS/INS navigation system. For LVN applications, Non-Holonomic Constraints (NHC) is one of the most common types of auxiliary information.This paper focuses on studying the contributions of the NHC from the perspective of observability, which provides a deeper insight and shows how the NHC improves the navigation solutions. Considering several typical vehicle dynamics, it is also clear to see the effects of the NHC to the inertial navigation under different situations. Both theoretical analysis and simulation tests have shown that the contributions of the NHC to the estimation of a certain state depend on both the current vehicle dynamic and the relative error magnitude of this state compared to the coupled state under the current vehicle dynamic; both the accelerating and turning motions can enhance the contributions of the NHC to the estimation of both the yaw and the pitch, and such contributions will be stronger with a higher vehicle speed; the NHC has significant effects on controlling the roll in all motion status. Furthermore, the effects of the NHC on the estimation of the biases of both gyroscopes and accelerometers are also analyzed. The outcomes of this paper show that the proposed observability analysis is beneficial to the utilization of NHC or other priori information in low-cost navigation systems.