The navigation system is undergoing dramatic changes, since the first global navigation system (GPS) and the satellite navigation has significantly developed to include GLONASS, Galileo and BeiDou. These Four constellation bring a great opportunity for scientific and engineering application with increase number of satellite and spatial geometry the accuracy, convergence time ,reliability and continuity could be improve.This project will investigate the performance of multi constellation (GPS, GLONASS, Galileo and Beidou) and multi frequency in term of high accuracy and precisions and comparison between the constellation and finding the best combination. High positioning technique will be used namely precise point positioning (PPP) in static case, using single receiver to process pseudo range and carrier phase measurement up to millimetre accuracy.

This PhD looks into using visually impaired people’s personal data such as location history and preferences to provide them with a safe and personalised navigation solution. This PhD tries to identify the visually impaired people’s navigation requirements and needs and create a navigation based profile for each user so that each navigation journey is personalised to the user based on his/her user profile. In addition, the navigation solution considers the use of a low-cost and widely available navigation device which is smartphone. Further, this research will explore the extent to which the navigation of visually impaired people might be enhanced using the approach of this research in comparison to the approaches taken by the existing navigation solutions.

Our position on the surface of the earth is defined by horizontal coordinates, latitude and longitude, which is in the past, referenced to local datum while the vertical coordinates, orthometric height, is referenced to the vertical datum.  The unification of the two datum came with the advent of space technology which is based on the global datum.    Coordinates can thus be transformed from one datum to another through datum transformation.     This research is investigating different transformation models to derive set of transformation parameters for the Nigerian Geodetic Network to transform coordinates from global datum to local datum and vice versa.

With the evolving GNSS landscape, the IGS has started the Multi-GNSS Experiment to produce products for new constellations. However, at the moment these products can only be used in post-processing. In my research, a simulator of Multi – GNSS observations and Real – Time products has been developed to analyse the performance of GPS only, Galileo only and GPS plus Galileo Precise Point Positioning. The impact of different signals combined in the Ionosphere – Free combination and the potential of the Galileo E5 signal are analysed. Finally alternative methods of ionosphere delay mitigation are considered in order to ensure the best possible positioning performance.

This project aims to establish requirement metrics of CAV in localisation and develop integrated localisation solutions for CAV. The required localisation performance of CAV will consist of three main parts, which are satellite positioning, sensing and communication. The relevant parameters, such as accuracy, availability, cost etc., will be defined and set up. The integrated localisation solutions will then be realised by the advanced algorithms which aims to provide required positioning performance in real driving environment. All the requirement metrics will then be assessed via a prototype CAV platform to verify and refine the CAV parameters.

The focus of this Ph.D. is given by the new area that has emerged: the rise of the connected and automated vehicle. Although the concept is an old one that can be found in utopic/dystopic literature, early attempts to create driverless vehicles have existed before but only with the recent project sponsored by DARPA, the driverless car has stepped closer towards becoming a reality. Appropriate levels of trust are an integral part in the successful deployment of such vehicles, especially since the trust of a user in the automated system is crucial for its use, disuse or abuse. Although some of the technologies that are incorporated into driverless cars have already existed in other fields such as aviation (autopilot), the driver-vehicle-environment system has specific characteristics and consequently, knowledge from other areas does not necessarily transfer. 

Kai Guo graduated in Instrumentation Science and Technology at Beihang University, China, in 2017. With a background on electrical engineering, he conducted research on robust satellite navigation in the presence of ionospheric scintillation during his master's. He is now a Marie Skłodowska-Curie research fellow at NGI within the TREASURE project ( www.treasure-gnss.eu), where he concentrates on developing novel GNSS receiver tracking models aiming to improve the receiver tracking performance under ionospheric scintillation. Mitigation tools will also be studied to realize robust GNSS under the adverse influence of scintillation. He will carry out his research in synergy with other research projects integrated in TREASURE.

This research focuses on developing a multiple algorithm approach to the analysis of GNSS time series for detecting geohazards and anomalies. His research studies the GNSS time series in spatial, temporal and spatio-temporal domains using Machine learning, signal processing and spatial analysis algorithms. His approach was examined using three different applications (1) The analysis of the GNSS coordinate time series of the GEONET network in Japan, corresponding to the Tohoku-Oki 2011 Mw9.0 earthquake (2) The analysis of the long-term GNSS coordinate time series of the BIGF network in the British Isles. (3)The analysis of the long-term GNSS troposphere time series of the BIGF network in the British Isles.

Qiyi received her B.Sc degree in software engineering and BA degree in English in China, and M.Sc degree in advanced computer science (software engineering) from the University of Manchester. She is currently in her first year of her PhD in Civil Engineering at the University of Nottingham. Her research is now mainly on connected and autonomous vehicle cyber security, which includes machine learning, cryptography and other relevant fields. 

In recent decades, the oil industry has contributed significantly to land cover change within the Niger Delta region of Nigeria. However, the true extent and severity of the land cover change due to oil production and exploration activities remains unclear. Therefore, the aims of this project are:

• To detect and model the magnitude, nature, direction and the likelihood of land cover changes in the Niger Delta.
• To determine and model the relationship between oil activities and the land cover changes.
• To model the proportional change in land cover due to variation in the amount of oil extraction

The research focuses on the development of processing strategies for monitoring greenhouse gas emissions using various satellite datasets to support climate policy and data-driven perspectives on industrial processes in South Africa. It makes use of space-based imagery products for the inference of methane, carbon dioxide, and nitrous oxide emissions and the identification of point sources. It further assesses the relationships between emissions and meteorological conditions, population growth, economic growth, and vegetation cycles.

The research is meant to contribute a decision-support tool to South Africa's Net Zero and climate change mitigation plans. The expectation is that it will help policymakers to identify geographic regions that are major emitters, enabling them to put measures into place to mitigate these emissions. 

Assessing building performance related to energy usage requires knowledge of occupancy patterns together with a lot of other behavioral factors. Due to the limitations of passive sensors in detecting an individual's occupancy throughout the building, indoor positioning (IP) techniques can provide a viable alternative but requires a robust positioning solution to be deployed. This creates an opportunity for research in novel application areas of indoor positioning to solve real-world problems of detecting building occupancy. 

The interdisciplinary nature of this PhD focuses on how we can bring together existing IP elements and demonstrate its contribution to the development of Building Occupant Models to understand building energy usage. It requires the development of an IP system which can be used to detect our intricate pattern of occupancy not just limited to private office space but rather spanning the entire building including corridors, hallways, stairs, and kitchens.

Sand movement is the main environmental issue in Sudan, leading to hazards such as the burial of cultural heritage sites. In this research, we will use remote sensing techniques (i.e. satellite images and aerial images) and land surveying techniques (i.e. GNSS, total station and levelling instruments) to understand and monitor sand movement changes, with respect to the influencing factors and its impact on urban areas, crop fields, forests, water bodies and archaeological sites. In addition, this research will focus on studying the effect of the various heights of sand dunes on the horizontal sand movement rate. What is expected from this research is to develop a modelling technique that uses the height of sand dunes to model sand movement. 

My research explores effective integration of Global Navigation Satellite Systems (GNSS), Inertial Navigation System (INS) and other sensors to meet the requirements for use in small, low-mass (sub 20 kg) Unmanned Aerial Vehicles (UAVs), whilst still addressing the challenges of the required performance. A key focus is on the addition of the input from the Vehicle Dynamics Model (VDM) and control surfaces.

Cristina’s current research interests focus on the mapping and characterisation of hydrocarbon seepage on both land and in the ocean using Earth Observation (EO) techniques and datasets. For her PhD research, she is using both Synthetic Aperture Radar (SAR) and optical datasets in the development of a fully automated workflow that enables the detection and mapping of oil slicks and estimation of the natural oil input into the environment.

She also has an interest in oil spill and geohazards mapping and analysis (e.g. earthquakes) using EO data, open science, innovation initiatives and the use of emerging technologies in EO (e.g. artificial intelligence and machine learning, Internet of Things). 

Brian's interest in high accuracy real-time GNSS research and background in geodetic surveying developed into his Engineering Doctorate which is funded through the TREASURE project (www.treasure-gnss.eu) as a Marie Skłodowska-Curie fellow. Brian's focus within the project is on PPP and RTK algorithm development and will involve multi-national collaboration with other TREASURE fellows, academic institutions, and industry leaders that produce advanced GNSS solutions. Specifically, Brian will integrate new error modelling and mitigation techniques to develop innovative, robust, multi-constellation PPP and RTK algorithms. 

Due to the limited user focus presented in existing leisure walking recommendation research, this project looks to investigate the curation of leisure walking experiences through user engagement with place. The work will investigate the development of a framework for leisure walking recommendation that can support the experiential nature of walking.

The initial aim of the study is investigating which engagements occur, what data is generated, and why individuals include each place of a walk. The data will then be used to investigate the scalability and reusability of the results and how this data can be augmented through existing datasets. 

Although GNSS technologies are used broadly in structural health monitoring of stiff civil engineering structures, the relatively high cost of GNSS receivers adopted are restraining their applications. Therefore, the aim of my project is to investigate the accuracy of low-cost GNSS receivers in monitoring low and high frequency motion and evaluate the potentials of low-cost GNSS receivers in monitoring civil engineering structures. The investigation will first focus on defining noise characteristics of the low-cost GNSS receivers experimentally, then the low-cost GNSS receivers will be assessed in monitoring the motion based on the motion frequency in the lab followed by data analysis and real infrastructure monitoring.