Drone Intention Prediction for Surveillance System PhD

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Fully funded PhD opportunity in Aerospace AI. Sponsored by EPSRC and Saab UK covering tuition, fees and a bursary of up to £20,568 (tax free) + research consumable costs. Drone intention prediction is vital to defending assets from uncooperative drones, where the research will focus on inverse learning and radar data processing.

Drones are changing in shape, design, algorithms, and operation control. Understanding their internal states is important for predicting threat to infrastructure and assets. Short term conflict alerting for primary surveillance system (e.g., radar systems) requires not only detection and classification, but also inference of mission intent.

The PhD will focus on working with academics and the sponsoring company Saab (UK) and Saab Sweden researchers in:

Developing robust intention prediction machine learning algorithms such as inverse reinforcement learning.
Using synthetic and real radar data to solve real problems.
Developing the evidence base to inform partners in radar and policing.
Working with partners on case studies 5. Writing high quality academic papers.

References of our past work are here:

“Uncovering Drone Intentions using Control Physics Informed Machine Learning,” Nature Communications Engineering, vol.3, Feb 2024.
“Uncovering Reward Goals in Distributed Drone Swarms using Physics-Informed Multi-Agent Inverse Reinforcement Learning,” AIEEE Transactions on Cybernetics, Nov 2024.
“Closed-Loop Output Error Approaches for Drone’s Physics Informed Trajectory Inference,” IEEE Transactions on Automatic Control, 2023.

You will be working world-leading scholar, Professor Weisi Guo, on the topic partnered with one of the industrial giants from the aerospace sector (Saab).

The work is envisioned to have great impact on design and development of intelligent radar data processing modules and inform both air surveillance and police stakeholders.

Fully funded PhD covering not only tuition, fees and bursary but opportunity to attend conferences and to link with industrial experts in the field.

The applicant is envisioned to further enhance and develop world class skills in AI and Machine Learning with application to hard and challenging drone problems providing a great skill set for employability after the degree in both industry but also academia as well.

At a glance

Application deadline: 30 Apr 2025
Award type(s): PhD
Start date: 02 Jun 2025
Duration of award: 3 years
Eligibility: UK
Reference number: SATM534

Entry requirements

Applicants must have a B.Sc. in electronic / information engineering or computer science or a related area and must either have or close to having a Master’s degree (must be completed by the time of the start of the PhD Award). A demonstrated background in aerospace dynamic models, control theory, radar signal processing and/or reinforcement learning would be a distinct advantage.

Funding

Sponsored by Sponsored by EPSRC, Saab UK and Cranfield University, this DTP studentship will provide a bursary of up to £20,568 plus home fees* and research costs for 3 years.

This studentship is open to primarily UK applicants at UK home fee only. However, we are only permitted to offer a limited number of studentships to applicants from outside the UK. Funded studentships will only be awarded to exceptional candidates due to the competitive nature of the funding.

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.