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Scheduling Surveillance of Space Objects (EPSRC CDT in Distributed Algorithms)

EPSRC CDT in Distributed Algorithms

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Prof J Ralph , Dr R MacKenzie No more applications being accepted Competition Funded PhD Project (European/UK Students Only)

About the Project

Are you looking for a challenging and exciting fully funded PhD in data science focused on reinforcement learning and GPU implementation while providing opportunities to test solutions with existing sensor systems?

Satellite operations provide services to the UK that underpin most aspects of modern-day life, from navigation to communication to banking, and therefore the continuation of these services is of critical importance to both the UK government and to the public. Since the start of the space age, Earth’s orbit has been becoming increasingly congested with debris from previous launches, break-up events, collisions and other mission related activities. To mitigate the risk of collisions between active satellites and debris there is an increasing emphasis on the enhancement of sensor networks to survey space objects in order to understand the composition of orbital debris, and to inform operators of the potential need to perform collision avoidance manoeuvres to ensure on-orbit safety. The research within this PhD supports long term UK prosperity and has applications in commercial, civilian and military spheres.

The aim of this project is to develop cutting edge sensor surveillance strategies to observe space objects which can outperform existing techniques by developing high quality, efficient, non-myopic sensor management algorithms to control space-surveillance sensors. These algorithms are required to maximise the value gained from limited sensor resources to enhance the understanding of the composition of space objects in orbit, specifically using ground-based optical telescopes. These surveillance strategies should have the ability to detect and respond in the instances that a satellite manoeuvre was performed and/or a collision event has occurred, to ensure that the correct data is promptly collected to enable mitigation actions.

Sensor management algorithms typically use Bayesian information theoretic approaches to evaluating the “utility” or “value” of different combinations of sensor and platform actions. Even when such action combinations are evaluated over a single discrete time-step (“myopic” approaches), the problem begins to suffer from combinatorial explosion as the number of actions enlarges. In order to achieve high quality task choices, non-myopic approaches are required; these approaches are able to trade off short-term gain for higher long-term gain. Non-myopic approaches are able to identify and mitigate for real-world challenges. In general, as the number of timesteps over which the solution is optimised increases, the identified solution will approach a globally optimal solution.

Due to the high levels of computational complexity, a key focus of the project is likely to be on understanding the potential to exploit multi-core computing hardware, such as GPUs.

Whilst there is extensive literature on myopic sensor management techniques and on optimising online decision making in deterministic contexts (e.g. the use of stochastic roll-out used with reinforcement learning for AlphaGo), the non-myopic sensor management problem remains under-researched. Therefore, this provides a great deal of potential exploration to improve the effectiveness of such approaches.

For information technical queries please contact Jason Ralph [Email Address Removed] or Rebecca MacKenzie [Email Address Removed]

For general application process queries contact [Email Address Removed]

To apply for this Studentship please follow the DA CDT Application Instructions: Submit an application for an Electrical Engineering PhD via the University of Liverpool’s online PhD application platform ( and provide the studentship title and supervisor details when prompted. Should you wish to apply for more than one project, please provide a ranked list of those you are interested in.

For a full list of the entry criteria and a recruitment timeline (including interview dates etc), Please see our website

Funding Notes

This project is a fully funded PhD for 4 years in total and will provide UK/EU tuition fees and maintenance at the UKRI Doctoral Stipend rate (£15,285 per annum, 2020/21 rate).


This project is part of the EPSRC Centre for Doctoral Training in Distributed Algorithms: The What, How and where of Next-Generation Data Science and has been developed in partnership with STFC Hartree and the UK’s Defence Science and Technology Laboratory (Dstl)

Dstl ensures that innovative science and technology contribute to the defence and security of the UK. This specific topic seeks to inform UK government (notably MOD and UK Space Agency) on the needs for Space Situational Awareness systems and help shape our investment to help ensure the UK continues to benefit from space delivered services. The collaboration with Dstl will establish a direction for the research by helping to define the research problem and to provide the opportunity to explore real-world scenarios by sharing access to historic data. Subject to nationality and security clearance, there will be an opportunity to undertake placements with Dstl, work along-side MOD scientists and opportunities to access and integrate solutions with existing telescopes to test the theoretical aspects of this work and collect real data that can be used to validate techniques.

Students are grouped in cohorts and based at the University of Liverpool and every project within the centre is offered in collaboration with an industrial partner who as well as providing co-supervision and placements will also offer the unique opportunity for students to access state of the art computing platforms, work on real world problems, benchmarking and data. Our graduates will gain unparalleled experiences working across academic disciplines in highly sought-after topic areas, answering industry need. The centre has a dedicated programme of interdisciplinary research training including the opportunity to undertake online modules in data science at UC Berkeley. A large number of events and training sessions are undertaken as a cohort of PhD students, allowing you to build personal and professional relationships that we hope will lead to research collaboration either now or in your future.

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