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

Practical implementation and performance assessment of an extended Kalman filter-based signal tracking loop

In this paper, the structure of a tracking loop with Extended Kalman Filter (EKF) is analyzed. Particular emphasis is given to the NCO update rule, which is seldom mentioned or studied in previous literature. Furthermore, the structure of an EKF-based software receiver is proposed including the special modules dedicated to the initialization and maintenance of the tracking loop. The EKF-based tracking architecture has been compared with a traditional one based on an FLL/PLL+DLL architecture, and the benefit of the EKF within the tracking stage has been evaluated in terms of final positioning accuracy. Further tests have been carried out to compare the Position-Velocity-Time (PVT) solution of this receiver with the one provided by two commercial receivers: a mass-market GPS module (Ublox LEA-5T) and a professional one (Septentrio PolaRx2e@). The results show that the accuracy in PVT of the software receiver can be remarkably improved if the tracking is designed with a proper EKF architecture and the performance we can achieve is even better than the one obtained by the mass market receiver, even when a simple one-shot least-squares approach is adopted for the computation of the navigation solutio

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

GNSS Integrity Monitoring assisted by Signal Processing techniques in Harsh Environments

The Global Navigation Satellite Systems (GNSS) applications are growing and more pervasive in the modern society. The presence of multi-constellation GNSS receivers able to use signals coming from different systems like the american Global Positioning System (GPS), the european Galileo, the Chinese Beidou and the russian GLONASS, permits to have more accuracy in position solution. All the receivers provide always more reliable solution but it is important to monitor the possible presence of problems in the position computation. These problems could be caused by the presence of impairments given by unintentional sources like multipath generated by the environment or intentional sources like spoofing attacks. In this thesis we focus on design algorithms at signal processing level used to assist Integrity operations in terms of Fault Detection and Exclusion (FDE). These are standalone algorithms all implemented in a software receiver without using external information. The first step was the creation of a detector for correlation distortion due to the multipath with his limitations. Once the detection is performed a quality index for the signal is computed and a decision about the exclusion of a specific Satellite Vehicle (SV) is taken. The exclusion could be not feasible so an alternative approach could be the inflation of the variance of the error models used in the position computation. The quality signal can be even used for spoofinng applications and a novel mitigation technique is developed and presented. In addition, the mitigation of the multipath can be reached at pseudoranges level by using new method to compute the position solution. The main contributions of this thesis are: the development of a multipath, or more in general, impairments detector at signal processing level; the creation of an index to measure the quality of a signal based on the detector’s output; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; An alternative method to compute the Position Velocity and Time (PVT) solution by using different well known algorithms in order to mitigate the effects of the multipath on the position domain