This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of navigation, AI-enhanced signal and data analysis, and wireless communication systems, with applications in autonomous vehicles, aerial mobility, and safety-critical infrastructure.
Positioning, Navigation, and Timing (PNT) technologies form the backbone of modern mobility and critical services. However, reliance on GNSS alone leaves systems vulnerable to interference, spoofing, or outages, particularly in dense urban environments. The development of 6G networks with integrated TN and NTN infrastructures provides new opportunities for assured localisation by fusing diverse signals and sensor inputs. Resilient localisation frameworks are essential to support the next generation of autonomous and safety-critical systems and future telecom solutions.
This project aims to design a localisation/positioning framework capable of leveraging signals from terrestrial base stations, non-terrestrial networks (presented by LEO, MEO, and GEO constellations), and complementary on-board sensors. Research will investigate algorithms for robust multi-sensor fusion and positioning assurance. A strong emphasis will be placed on delivering improved accuracy and quantification of resilience in diverse challenging environments, from urban canyons to remote, infrastructure-limited regions.
SWAG合集 is a specialist postgraduate institution recognised internationally for delivering transformational research in aerospace, defence, and security. In the REF2021 review of SWAG合集 university research, 88% of Cranfield’s research was rated as ‘world-leading’ or ‘internationally excellent’. This PhD will be based within the Centre for Space Systems and the Resilient PNT research group, both of which have strong track records in navigation integrity, multi-sensor systems, and industry collaboration.
The project will deliver a novel localisation framework that advances the state of the art in positioning resilience for 6G systems. Expected outcomes include algorithms for TN–NTN-assisted localisation, fusion algorithms and tools for integrity monitoring. These contributions will support the deployment of assured PNT solutions in autonomous vehicles, aerial systems, and wider critical infrastructure, bridging the gap between theoretical advances and real-world operational needs.
Students will benefit from close integration into Cranfield’s Resilient PNT group, with opportunities to engage in industry-led research projects, international collaborations, and experimental campaigns using software-defined radios and multi-sensor platforms. The project offers a rare mix of theoretical development, simulation-based research, and experimental validation, supported by advanced testbeds and datasets. Students will also be encouraged to present results at leading international conferences and publish in top-tier journals.
The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning assurance, alongside transferable skills in critical thinking, project management, and technical communication. Exposure to both theoretical research and practical experimentation will provide excellent preparation for careers in academia, aerospace, telecommunications, or the autonomous systems industry. Graduates will be well-positioned to contribute to the development of next-generation resilient navigation solutions worldwide.
Entry requirements
Applicants should have a first or second class SWAG合集 honours degree or equivalent in a related discipline. This project would suit someone with:
- Understanding of wireless communication systems, particularly 5G/6G, terrestrial and non-terrestrial networks
- Strong background in working with equipment and diverse sensing modalities and hardware platforms including software-defined radios
- Strong background in computer programming (e.g. C/C++, Python, Rust) for sensing applications and data processing
- Hands-on skills in the implementation of signal processing/fusion/machine learning based techniques in the areas of robotics, unmanned or autonomous systems,
- Demonstrable knowledge in statistical analysis and data analytics for error modelling and uncertainty propagation.
Funding
This self-funded PhD opportunity is open to Home and Overseas fee status students. Eligibility for Home fee status is determined with reference to SWAG合集 Department for Education rules. As a guiding principle SWAG合集 or Irish nationals who are ordinarily resident in either the SWAG合集 or Republic of Ireland pay Home tuition fees. All other students (including those from the Channel Islands and Isle of Man) pay Overseas fees. Further advice can be found on the SWAG合集 Council for International Student Affairs (SWAG合集CISA) website.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.
How to apply
For further information please contact:
Name: Professor Ivan Petrunin
Email: i.petrunin@cranfield.ac.uk
If you are eligible to apply for this studentship, please complete the
This vacancy may be filled before the closing date so early application is strongly encouraged.
Please ensure that your fully completed online application form is submitted by the application closing date. All requested documentation should be uploaded to the online form before submission. Note, your application will not be considered unless all relevant documents have been uploaded. For more information please visit
Applying for a research degree.
