Trust in Distributed Surveillance Systems

   Department of Automatic Control and Systems Engineering

   Saturday, August 03, 2024  Competition Funded PhD Project (UK Students Only)

About the Project

Are you interested in Machine Learning and Signal Processing methods and their applications to autonomous systems and surveillance? We have a PhD project funded by the UK Government Defence Science and Technology Laboratory (DSTL) at the University of Sheffield, a leading World University and a Russell Group University in the UK.

This is an excellent opportunity for professional growth and acquiring machine learning skills in a rapidly developing area and apply them to important engineering applications for sensor management and distributed systems. Modern sensor networks for defence and security can generate very large amounts of data. These networks are often comprised of sensors with different modalities, such as radar, acoustic sensors, LIDAR, combined with optical and thermal cameras. Moreover, data arrive at different rates and with varying levels of accuracy. Making sense of such multiple heterogeneous data is a challenging task that has been extensively studied, but the provision of reliable solutions for autonomous and semi- autonomous systems is a task that remains only partially solved.

In autonomous surveillance systems, machine-learning algorithms play a crucial role since their outputs are integrated into various downstream tasks. The development of learning frameworks that include an awareness of their limitations and have the capacity for insightful introspection in changeable environments is an essential part of this project. These data-driven algorithms must assess their performance, predict incipient breakdown, and continually learn from large streaming datasets. In addition, these systems must operate within the energy budgets in (near) real-time scenarios.

This project therefore aims to develop trustworthy methods for autonomous inference, situation awareness and sensor management which are robust, interpretable and actionable in complex situations. A special emphasis will be given on Gaussian process methods that are able to learn their hyperparameters and operate under a range of changeable conditions – not only with different measurement noise uncertainties, but also environmental factors. Sensor networks which are formed by mobile and static sensors will be considered, with different types of sensors, including cloud computing. Reinforcement learning methods are also potential candidates for the considered sensor management tasks.

The main objectives of this project are:

1) to develop approaches for inference and tracking that are modular, and scalable with respect to the volume of data arising from the number of targets (and so states estimated), data sampling rates, and numbers of sensors and their geographic extent;

2) to quantify the impact of uncertainties with respect to measurement noise, dynamical change in the environment, abrupt target manoeuvres among other factors; to provide reliable solutions, with a defined level of trust - these uncertainty- aware algorithms can self-assess performance and continually learn from the available datasets while operating in real-time on a limited energy budget;

3) to develop methods for sensor management and data fusion linked with inference and intent prediction, jointly applied with tracking.

The project is multidisciplinary and requires knowledge from signal processing, sensor networks, probabilistic methods and machine learning.

Supervised by Prof. Lyudmila Mihaylova and in support of an expert from, the PhD student will be based at the Department of Automatic Control and Systems Engineering at the University of Sheffield. You will join the group of Prof. Mihaylova and have the opportunity to interact with other researchers working in the areas of autonomous systems, machine learning and trustworthy intelligent systems.

The Department is a world-leading Centre devoted to Control and Systems Engineering and one of the largest departments dedicated to the subject in Europe. ACSE was an integral part of the recent REF submission where 96% of the Faculty of Engineering's research was rated as either world-leading or internationally excellent and with the quality of our research environment rated as top nationally.

The University of Sheffield is therefore a vibrant, innovative and supportive place to undertake research.

For additional information, please feel free to contact Prof. Lyudmila Mihaylova with your CV and relevant qualifications /transcripts.

Please note start dates up to 1st of October 2024 are available.

Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

The PhD position covers funding for 4 years paying for home tuition fees, and an enhanced stipend rate, starting at £22,998 with yearly incremental increases. In
addition, substantial funding is available for the successful candidate to attend national and international workshops, conferences and networking activities, meeting world leading experts in the area and for knowledge exchange. The available travel support is £27,600.

How good is research at University of Sheffield in Engineering?

Research output data provided by the Research Excellence Framework (REF)

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