This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.
The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training programme that will equip up to 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.
This studentship is open to UK nationals only.
The successful PhD student will be co-supervised and work alongside our external partner Leonardo.
The ability to benefit from combining data from multiple sensors is a function of the models for the behaviour of the targets under surveillance: put simply, the better the behavioural models, the greater the benefit of sensor fusion. Existing models are simple, and typically assume that the target moves according to a nearly-constant velocity model, ie integrated Brownian motion. Any improved behavioural models need to be flexible enough to describe the range of behaviours and target-generated phenomenology (eg related to the use of deception) that are encountered but also deterministic enough both to distinguish target-like trajectories from sequences of false alarms and to extract useful meta-data from the trajectory data (eg when any deception was being deployed).
The availability of, albeit largely asynchronous, historic datasets from each of multiple sensors and the power of machine learning should, in theory, make it possible to learn such models from the data. However, this has not been explored extensively in the past. This is because applying machine learning to such datasets is challenging: the core challenge is to learn the dynamic models efficiently while also capitalising on the well-understood statistics associated with modelling non-linear measurements, missed detections and false alarms. Thankfully, techniques (involving extensions to Particle-MCMC that calculate and then capitalise upon gradient information and can also make use of modern compute resources typically used for Deep Learning) have recently been developed that are applicable to this specific kind of machine learning problem.
This PhD will seek to adapt these novel techniques to the learning of behavioural models from historic multi-sensor surveillance data and will evaluate the utility of the learned behavioural models in the context of generic multi-sensor fusion contexts and specific contexts relevant to Leonardo and anticipated to include the fusion of electronic surveillance, radar and EO/IR data.
Application Web Address:
Visit the CDT website for funding and eligibility information.
You must enter the following information:
· Admission Term: 2022-23
· Application Type: Research Degree (MPhil/PhD/MD) – Full time
· Programme of Study: Electrical Engineering and Electronics – Doctor in Philosophy (PhD)
The remainder of the guidance is found in the CDT application instructions on our website.