AimDevelop new particle filter solutions for tracking of interacting multiple targets.ObjectivesDevelop new interacting particle filters that can tackle multi-target tracking in a scalable mannerUnderstand the hidden low dimensional structures within multi-target systems to enable high dimensional scalable Monte Carlo filteringExplore how these models can further be used to predict intent for groups of cooperating and/or adversarial targets.DescriptionHigh-dimensional multi-target tracking poses severe computational challenges for sequential inference. Although particle filters provide a principled Monte Carlo framework, their efficiency collapses with dimension. This project aims to identify and exploit hidden low-dimensional structures within multi-target systems, enabling adaptive filtering methods that retain Monte Carlo flexibility while mitigating the curse of dimensionality. We will develop local particle filters, block-based filtering, and topology discovery methods to automatically detect weakly interacting groups of targets. We may consider scenarios involving tens to a few hundred targets. At this stage, we will primarily use simulated data, with the flexibility to incorporate real data from partners in later stages. The outcome will be scalable algorithms for structured high-dimensional tracking, combining statistical efficiency with real-time feasibility in sensing and defence applications. Closing date: Sat, 31/01/2026 - 12:00 Apply now Principal Supervisor Prof Victor Elvira Arregui Assistant Supervisor Prof Mike Davies Eligibility Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. This project requires a student with a high level of mathematics or numeracy.Further information on English language requirements for EU/Overseas applicants. Funding Full funding is available for this project.
