An ARAIM adaptation for Kalman Filter

This work propose a new Kalman filter-based method for integrity monitoring, following the solution separation approach of the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm. This method evaluates the separation of state correction using different subsets of measurement to detect abnormalities as well as potential faulty satellites for exclusion. This approach differs from existing Kalman filter-based methods, which use innovation vector or residual vector for stochastic evaluation

Integrity Monitoring Using ARAIM Algorithm in Urban Environment

Aviation is one of the earliest application of the Global Navigation Satellite System (GNSS). Since the early days of the Global Positioning System (GPS), satellite navigation has been an essential part of the aviation industry. Being a particular mean of transport, which usually involves a large number of human lives, civil aviation always requires a high level of reliability from the navigation system. Such requirement brings about the concept of integrity, which concerns about the consistency and reliability of a navigation system, is defined as the capability of the system to provide timely warning when it should not be used for navigation. The concept of integrity allows the standardization of guidance systems' performance, with the utmost purpose of keeping safety for every flight. The concept of integrity has gained interests in other GNSS applications as well, especially in those that also require high reliability from the navigation solution, such as Intelligent Transport System (ITS), railways. This leads to the necessity to adapt the integrity monitoring techniques, in particular the Receiver Autonomous Integrity Monitoring (RAIM) algorithms, to use in working conditions other than the typical airport areas, such as urban environment. As a matter of fact, adaptation of RAIM algorithms to urban environment requires a throughout analysis of the environmental difference of the working condition as well as the requirement of the intended applications. This thesis focuses on developing a Kalman filter-based Advanced RAIM (ARAIM) algorithm for urban environment, which is an adaptation of the conventional ARAIM algorithm for civil aviation. ARAIM algorithm is considered the next generation of RAIM, aiming at providing higher integrity performance for more stringent phase of flight. The first step is to survey the necessary changes to adapt ARAIM algorithm to urban scenario. Experimental study highlights the prerequisite of finding a noise model to represents the signal noise level in urban area. After a suitable noise model was found after a comparative study, the KF-based ARAIM algorithm was developed. This method evaluates the separation of state correction using different subsets of measurement to detect abnormalities as well as potential faulty satellites for exclusion. The proposed method was also validated using simulation and real data. Performance analysis results show that the proposed algorithm can effectively follows the changes of signal quality which is expected to occur frequently when moving in urban environment, confirming its suitability for integrity monitoring in urban environment

A New Cooperative PPP-RTK System with Enhanced Reliability in Challenging Environments

Compared to the traditional PPP-RTK methods, cooperative PPP-RTK methods provide expandable service coverage and eliminate the need for a conventional expensive data processing center and the establishment and maintenance of a permanently deployed network of dense GNSS reference stations. However, current cooperative PPP-RTK methods suffer from some major limitations. First, they require a long initialization period before the augmentation service can be made available from the reference stations, which decreases their usability in practical applications. Second, the inter-reference station baseline ambiguity resolution (AR) and regional atmospheric model, as presented in current state-of-art PPP-RTK and network RTK (NRTK) methods, are not utilized to improve the accuracy and service coverage of the network augmentation. Third, the positioning performance of current PPP-RTK methods would be significantly degraded in challenging environments due to multipath effects, non-line-of-sight (NLOS) errors, poor satellite visibility and geometry caused by severe signal blockages. Finally, current position domain or ambiguity domain partial ambiguity resolution (PAR) methods suffer from high false alarm and miss detection, particularly in challenging environments with poor satellite geometry and observations contaminated by NLOS effect, gross errors, biases, and high observation noise. This thesis proposed a new cooperative PPP-RTK positioning system, which offers significant improvements to provide fast-initialization, scalable coverage, and decentralized real-time kinematic precise positioning with enhanced reliability in challenging environments. The system is composed of three major components. The first component is a new cooperative PPP-RTK framework in which a scalable chain of cooperative static or moving reference stations, generates single reference station-derived or reference station network-derived state-space-representation (SSR) corrections for fast ambiguity resolution at surrounding user stations with no need for a conventional expensive data processing center. The second component is a new multi-feature support vector machine (SVM) signal classifier based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. The third component is a new PAR method based on machine learning, which employs the combination of two support vector machine (SVM) to effectively identify and exclude bias sources from PAR without relying on satellite geometry. The prototype of the new PPP-RTK system is developed and substantially tested using publically available real-time SSR products from International GNSS Service (IGS) Real-Time Service (RTS)

Trustworthy precise point positioning with global navigation satellite systems

With the modernization of the Global Navigation Satellite System (GNSS), GNSS precise point positioning (PPP) technology becomes popular benefiting from its wide coverage and high accuracy. However, PPP technology still has many challenges in terms of continuity, fast convergence, and integrity monitoring, and these unsolved issues result in limitations of engineering applications. In this thesis, a reliable PPP technology with GNSS is investigated. The main contributions of the thesis are as follows: (1) A new cycle slip repair method that uses multiple epochs of time-differencing and geometry-based observations are proposed which has a significant improvement in the success rate of cycle slip repairs compared to existing methods. The positioning results also reflect that this method can reduce position errors and improve the continuity of PPP technology. (2) A systematic comparison of current interpolation methods used for high-accuracy regional ionospheric corrections is presented. It is found that each method has essentially the same accuracy in a small regional network with only a few stations, while the Kriging interpolation method can significantly improve the accuracy when the size of the network increases. Besides, a new method for predicting the uncertainty after broadcasting by grid point is also proposed. It has been validated that it is significantly closer to reality than other existing methods. In addition, different ionospheric correction implementation methods at the user end are also compared. (3) A integrity monitoring scheme for use in PPP based on real-time kinematic (RTK) positioning networks (PPP-RTK) with regional atmospheric corrections has been developed, which is based on the impacts of faults on the estimators considering possible faults in undifferenced and uncombined measurements. (4) Procedures for integrity monitoring considering the risks caused by incorrect ambiguity fixing are investigated. Two different methods for considering the probability of wrong ambiguity fixing including categorizing it into unmonitored fault and categorizing it as an individual type of fault are proposed and analyzed. (5) An integrity monitoring (IM) scheme based on the single-epoch framework for PPP-RTK is also proposed in order to exclude the effects caused by using observations from multiple epochs. Different solutions and their related availability are evaluated based on the satellite geometry in the global area