Adaptive, reliable, and accurate positioning model for location-based services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new strategy in achieving highly reliable and accurate position solutions fulfilling the requirements of Location-Based Services (LBS) pedestrians’ applications. The new strategy is divided into two main parts. The first part integrates the available positioning technology within the surrounding LBS application context by introducing an adaptive LBS framework. The context can be described as a group of factors affecting the application behaviour; this includes environmental states, available resources and user preferences. The proposed adaptive framework consists of several stages, such as defining the contextual factors that have a direct effect on the positioning performance, identifying preliminary positioning performance requirements associated with different LBS application groups, and introducing an intelligent positioning services selection function. The second part of this work involves the design and development of a novel positioning model that is responsible for delivering highly reliable, accurate and precise position solutions to LBS users. This new model is based on the single frequency GPS Standard Positioning Service (SPS). Additionally, it is incorporated within the adaptive LBS framework while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates over a client-server architecture including two main components, described as the Localisation Server (LS) and the Mobile Unit (MU). Hybrid functional approaches were developed at both components consisting of several processing procedures allowing the positioning model to operate in two position determination modes. Stand-alone mode is used if enough navigation information was available at the MU using its local positioning device (GPS/EGNOS receiver). Otherwise, server-based mode is utilised, in which the LS intervenes and starts providing the required position solutions. At the LS, multiple sources of GPS augmentation services were received using the Internet as the sole augmentation data transportation medium. The augmentation data was then processed and integrated for the purpose of guaranteeing the availability of valid and reliable information required for the provision of accurate and precise position solutions. Two main advanced position computation methods were developed at the LS, described as coordinate domain and raw domain. The positioning model was experimentally evaluated. According to the reported results, the LS through the developed position computation methods, was able to provide position samples with an accuracy of less than 2 meters, with high precision at 95% confidence level; this was achieved in urban, rural, and open space (clear satellite view) navigation environments. Additionally, the integrity of the position solutions was guaranteed in such environments during more than 90% of the navigation time, taking into consideration the identified integrity thresholds (Horizontal Alert Limits (HAL)=11 m). This positioning performance has outperformed the existing GPS/EGNOS service which was implemented at the MU in all scenarios and environments. In addition, utilising a simulation evaluation facility the developed positioning model performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS/EGNOS. Using the statistical t-test, it was concluded that there is no significant difference in terms of the position samples’ accuracy achieved from the developed positioning model and the hybrid system at a particular navigation environment described as rural area. The p-value was 0.08 and the level of significance used was 0.05. However, a significant difference in terms of the service integrity for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions

Bewertung und Verifikation der Leistung der satellitenbasierten Zugortung

Global Navigation Satellite Systems (GNSS) are potentially applicable for various railway applications, especially the safety-related applications such as train localisation for the purpose of train control. In order to integrate GNSS for train localisation, a trustable stand-alone GNSS-based localisation unit should be developed. Then to comply with EN 50126 (reliability, availability, maintainability, and safety; RAMS) standards, the demonstration of GNSS quality of service (QoS) should be evaluated in consistent with RAMS. However there are currently no appropriate performance evaluation methods on GNSS for railway safety-related applications. This dissertation identifies the required performance for train localisation in consideration of GNSS QoS and railway RAMS. The common and different properties of the performance are analysed in detail using consistent attribute hierarchy structures based on UML class diagram. Then formalised performance requirements are proposed quantitatively on four properties (accuracy, reliability, availability, and safety integrity). After that, the evaluation and verification methodologies are introduced. The evaluation methodology is using a reference measurement system for GNSS receiver measured train location accuracy identification, and a stochastic Petri net (SPN) model for GNSS receiver measured train location accuracy categorisation. The SPN model illustrates the GNSS receiver measured train locations into three states (up state, degraded state, and faulty state). Then the four proposed properties are allocated and estimated formally using the three states in the SPN model. The verification methodology is used to verify the GNSS receiver measured train location in real time based on a localisation unit. The GNSS receiver measured train locations are verified using hypothesis testing methods based on the accurate digital track map provided beforehand. Then train location estimation from the localisation unit is verified according to the mileage of the train. With the verified train location estimation from the localisation unit, the corresponding safety margin for each train location is calculated. The data for evaluation and verification methodologies are collected from a test train running on a railway track in High Tatra Mountains. The results show an approach of the possible certification procedure for the GNSS receivers in railway safety-related applications.Globales Satellitennavigationssystem (GNSS) können für verschiedene Anwendungen im Schienenverkehr, vor allem für sicherheitsrelevante Anwendungen wie Zugortung zum Zweck der Zugsicherung gestützt werden. Um GNSS für Zugortung zu integrieren, muss eine eigenständige satellitenbasierte Ortungseinheit entwickelt werden. Um die Entwicklung in Einklang mit EN 50126 (Überlebensfähigkeit, Verfügbarkeit, Instandhaltbarkeit, und Sicherheit; RAMS) durchzuführen, muss der Nachweis der Güte von GNSS (Quality of Service; QoS) entsprechend in Einklang mit dieser Norm bewertet werden. Allerdings gibt es zurzeit keine RAMS Bewertungsverfahren für satellitenbasierte sicherheitsrelevante Anwendungen im Schienenverkehr. Diese Dissertation identifiziert die notwendigen Anforderungen für die Zugortung unter Berücksichtigung der Güte von GNSS und den bestehenden Normen bezüglich RAMS im Schienenverkehr. Die gemeinsamen und unterschiedlichen Eigenschaften der Anforderungen werden detailliert mit Nutzung einer Attributhierarchie basierend auf UML-Klassendiagrammen dargestellt. Danach werden formalisierte Leistungsanforderungen quantitativ für vier Eigenschaften (Genauigkeit, Zuverlässigkeit, Verfügbarkeit und Sicherheitsintegrität) vorgeschlagen. Darauf aufbauend werden die Bewertungs- und Verifikations- Methoden eingeführt. Die Bewertungsmethode nutzt ein Referenzmesssystem zur Identifikation der Zugortungsgenauigkeit der GNSS Empfänger und ein stochastischen Petri-Netz-Modell (SPN-Modell) für die Kategorisierung der GNSS Empfänger Zugortmessungen. Das SPN-Modell veranschaulicht die GNSS Empfänger Zugortmessungen in drei Zuständen (up state, degraded state, faulty state). Dann werden die vier vorgeschlagenen Eigenschaften zugeordnet und formal mit Nutzung der drei Zustände im SPN-Modell geschätzt. Die Verifikationsmethode wird verwendet, um die GNSS Empfänger Zugortmessungen in Echtzeit zu verifizieren. Die GNSS Empfänger Zugortmessungen werden mit einer Hypothesentestmethode auf der Grundlage der genauen digitalen Streckenkarte verifiziert. Mit der verifizierten geschätzten Zugortmessung wird der resultierende Sicherheitsbereich für jeden Zugort berechnet. Die Daten für die Auswertungs- und Verifikationsmethoden wurden von einem Zug im Regelbetrieb auf einer Eisenbahnstrecke in der Hohen Tatra gesammelt. Die Ergebnisse dieser Arbeit zeigen einen Ansatz der möglichen Zertifizierungsverfahren für die GNSS-Empfänger für sicherheitsrelevante Anwendungen im Schienenverkehr

Entwicklung intelligenter GNSS-basierten Landfahrzeug Lokalisierungssysteme

The usage of Global Navigation Satellites Systems (GNSS) for localisation purposes demands a permanent evaluation of the position information provided for the receiver, as well as a standardised GNSS-Receivers validation methodology and subsequently quality control procedures oriented to land vehicles within the ergodic hypothesis. The use of an independent reference system should provide enough information to validate the localisation system, but the lack of proper evaluation and procedures presents significant blind spots for future applications in both the GNSS-Receiver and the correspondent reference system. To solve these problems an approach based on artificial intelligence (AI) is presented. Also the development of an advanced filter technique for positioning estimation results in significant improvements of the reference system, even allowing a standalone GNSSdependent reference system when no independent systems are available. The presented developments are the bases for future intelligent GNSS-based localisation systems. The methodologies combine the advanced Particle Filter (PF) for positioning estimation with the newly developed Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation and the Artificial Neural Networks (ANN) models for both quantitative and qualitative validation. In this thesis the bases of the intelligent GNSS-based localisation system are presented and developed follows the BMW principle. In German the BMW principle stands for Beschreibungsmittel (means of description), Methode (methods) and Werkzeug (tool). The resulting system described along the thesis is applied and tested in a demonstrator tool, validating the developed methodologies in both software and hardware level. The proposed methodologies for the development of an intelligent GNSS-based localisation system are a substantial contribution for intelligent GNSS-based validation tools that will enable future safety-relevant applications, in field such as on-board uncertainty evaluation of vehicle localisation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective enabled-localisation.Die Nutzung der globalen Navigationssatellitensysteme (GNSS) zu Lokalisierungszwecken erfordert eine ständige Auswertung der generierten Positionsinformationen sowie eine standardisierte Validierungsmethodik und anschließende Qualitätskontrollverfahren der GNSS-Empfänger. Die Verwendung eines unabhängigen Referenzsystems sollte genügend Informationen liefern, um das Lokalisierungssystem zu validieren, aber das Fehlen sowohl einer angemessenen Auswertung als auch entsprechender Verfahren stellen erhebliche Lücken für zukünftige Anwendungen sowohl dem Empfänger und der Referenz dar. Um diese Probleme zu lösen, wird ein Ansatz mit Künstlicher Intelligenz (KI) vorgestellt. Die Entwicklung KI-basierter Validierungstools sowie Filtertechniken zur Positionsbestimmung, um das Bezugssystem zu unterstützen, führt zu erheblichen Verbesserungen insofern, als dass ein GNSS-abhängiges Referenzsystem erstellt werden kann, wenn keine unabhängigen Referenzsysteme verfügbar sein sollten. Diese zusätzlichen Elemente sind die Grundlagen für zukünftige intelligente GNSSbasierte Lokalisierungssysteme. Die vorgestellten Methoden vereinen fortschrittliche Partikelfilter (PF) für die Positionsbestimmung mit der neuentwickelten Mahalanobis-Ellipsen-Filter (MEF)-Methodik für die genauigkeitsbasierte Datenauswertung, sowie einen Künstlichen-Neuronalen-Netze (KNN)-Ansatz für sowohl qualitative als auch quantitative Validierungstools. Im Rahmen des BMW-Prinzips (kurz für Beschreibungsmittel, Methoden undWerkzeuge) werden die Grundlagen für ein KI-basiertes System für GNSS-basierte Lokalisierungssysteme vorgestellt und im Rahmen dieser Arbeit entwickelt. Das sich ergebende intelligente GNSS-basierte Lokalisierungssystem wird in einem Demonstrator-Werkzeug angewendet, um das entwickelte System auf der Software- und Hardware-Ebene zu validieren. Abschließend wird eine Risikoanalyse des Demonstrators präsentiert. Diese Methoden zur Entwicklung eines intelligenten GNSS-basierten Lokalisierungssystems werden zukünftige sicherheitsrelevante Anwendungen in Bereichen wie Bordunsicherheitsermittlung in der Fahrzeuglokalisierung, Fahrassistenzsysteme und GNSSbasierte Fahrzeugortung mit intelligenten Karten für eine spurselektive Lokalisierung ermöglichen

Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected in the UAV indoor and UGV outdoor environments

Automotive applications of high precision GNSS

This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas

Data Quality Assessment for Maritime Situation Awareness

International audienceThe Automatic Identification System (AIS) initially designed to ensure maritime security through continuous position reports has been progressively used for many extended objectives. In particular it supports a global monitoring of the maritime domain for various purposes like safety and security but also traffic management, logistics or protection of strategic areas, etc. In this monitoring, data errors, misuse, irregular behaviours at sea, malfeasance mechanisms and bad navigation practices have inevitably emerged either by inattentiveness or voluntary actions in order to circumvent, alter or exploit such a system in the interests of offenders. This paper introduces the AIS system and presents vulnerabilities and data quality assessment for decision making in maritime situational awareness cases. The principles of a novel methodological approach for modelling, analysing and detecting these data errors and falsification are introduced

Avionics-based GNSS integrity augmentation performance in a jamming environment

Intentional and unintentional radiofrequency interference (i.e., jamming) can result in degraded navigation accuracy or complete loss of the GNSS signal tracking. Jammers can be classified into three broad categories: Narrowband Jammers (NBJ), Spread Spectrum Jammers(SSJ) and Wideband Gaussian Jammers (WGJ). In recent years, a number of effective jamming detection and anti-jamming (filtering and suppression) techniques have been developed for military GNSS applications and some of them are envisaged to be used for civil purposes (e.g., terrorist attacks). The synergies between these jamming detection techniques and our newly developed Avionics-Based Integrity Augmentation (ABIA) system are investigated in this paper. In particular, GNSS vulnerability to NBJ, SSJ and WGJ types of jamming is analytically described in terms of Jamming to Signal (J/S) tracking thresholds and the models for calculating the minimum acceptable aircraft-to-jammer ranges are presented. Simulation results demonstrate that the proposed ABIA architecture is capable of performing jamming detection and avoidance when GNSS is considered as the primary source of navigation data

Europe's Space capabilities for the benefit of the Arctic

In recent years, the Arctic region has acquired an increasing environmental, social, economic and strategic importance. The Arctic’s fragile environment is both a direct and key indicator of the climate change and requires specific mitigation and adaptation actions. The EU has a clear strategic interest in playing a key role and is actively responding to the impacts of climate change safeguarding the Arctic’s fragile ecosystem, ensuring a sustainable development, particularly in the European part of the Arctic. The European Commission’s Joint Research Centre has recently completed a study aimed at identifying the capabilities and relevant synergies across the four domains of the EU Space Programme: earth observation, satellite navigation, satellite communications, and space situational awareness (SSA). These synergies are expected to be key enablers of new services that will have a high societal impact in the region, which could be developed in a more cost-efficient and rapid manner. Similarly, synergies will also help exploit to its full extent operational services that are already deployed in the Arctic (e.g., the Copernicus emergency service or the Galileo Search and rescue service could greatly benefit from improved satellite communications connectivity in the region).JRC.E.2-Technology Innovation in Securit

Low-cost navigation and guidance systems for unmanned aerial vehicles - part 1: Vision-based and integrated sensors

In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs)