Information theoretic methods for sensor management

About the project

This project will focus on developing algorithms to allow autonomous systems to make decisions based on information they receive from sensors. Your work will be applied to distributed systems which operate in challenging environments.

Since the emergence of the Internet of Things and other extensive sensor networks, algorithms that can efficiently manage communication, sensing, and energy resources are critical for effective inference under various limitations and/or availability constraints.

In this project, you'll develop intelligent sensing that will enable continuous and adaptive surveillance in dynamic environments. You'll build on the work of our research group, which looked at multi-targeting tracking and distributed sensor fusion, to develop new mathematical models and algorithms for autonomous systems. You will explore methods for allocating sensor resources and refining the knowledge of the scene.

The algorithms you develop will be based on mathematical concepts from probability theory, information theory, and statistics, with some connections to particle physics.

Potential supervisors

• Potential supervisors: Lead supervisor DC Dr Daniel Clark FRAeS, FIET, CMath FIMA, CStat, SFHEA, SMIEEE Professor

Lead supervisor

• Lead supervisor: DC Dr Daniel Clark FRAeS, FIET, CMath FIMA, CStat, SFHEA, SMIEEE Professor

Dr Daniel Clark FRAeS, FIET, CMath FIMA, CStat, SFHEA, SMIEEE

• Dr Daniel Clark FRAeS, FIET, CMath FIMA, CStat, SFHEA, SMIEEE: Professor

Entry requirements

A 2:1 honours degree, or its international equivalent in one of the following subject areas:

• mathematics
• physics
• electrical engineering
• aerospace engineering

Fees and funding

For UK students, tuition fees and a stipend of £17,668 tax-free each year for up to 3.5 years.

How to apply

Apply now

You need to:

Applications should include:

• choose programme type (Research), 2022/23, Faculty of Engineering and Physical Sciences
• choose PhD in Computer Science (Full time)
• add name of the supervisor (Daniel Clark) in section 2
• your cv (resumé)
• 2 reference letters
• degree transcripts to date

Faculty of Engineering and Physical Sciences

Email: feps-pgr-apply@soton.ac.uk

Project leader

If you wish to discuss any details of the project informally, please contact Dr Daniel Clark.

• Email: daniel.clark@soton.ac.uk
• Phone: +44 (0)23 8059 3715

Research areas