Non-myopic approaches to   sensing and surveying

This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

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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 course that will equip over 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.

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The successful PhD student will be co-supervised and work alongside our external partner Cubica Technology Ltd. Cubica develops data fusion and sensor management software and will provide guidance, example use-cases and scenarios, as well as the opportunity to integrate and test the developed algorithms with real UAS platforms.

This PhD project tackles the development of high quality but efficient multi-sensor non-myopic sensor management algorithms for controlling sensors and unmanned autonomous systems (UAS) such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). These distributed algorithms are needed in order to exploit the ever-growing capability of autonomous systems for security, monitoring and surveying applications. The algorithms are used to make decisions on things such as where the platforms travel, what direction the sensors are pointed in, and what configuration the sensing will take. 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 sensors is increased or the space of actions enlarges. In order to achieve high quality task choices, non-myopic approaches that consider different combinations of actions over multiple time-steps 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 such as obstacles (to gathering information). In general, as the number of timesteps over which the solution is optimised increases, the identified solution will approach a globally optimal solution. There is extensive literature on myopic sensor management techniques and some literature on applied non-myopic approaches. A large amount of prior and relevant work also exists in other areas of computer science and control theory such as the travelling salesman problem and receding horizon control. However, the non-myopic sensor management problem remains under-researched, in large part due to the associated computational challenges. Realised algorithms often only operate on short horizons (e.g. 2/3/4 time-steps) and as such there is still a great deal of potential to improve the effectiveness of such approaches. This project will investigate novel formulations of sensor management problems, as well as the use of Sequential Monte Carlo techniques for identifying high quality solutions. Due to the high levels of computational complexity, a key focus of the project is likely to be understanding the potential for exploiting multi-core computing hardware such as GPUs and/or FPGAs. Another area of interest is how such approaches can generate solutions as part of a human-machine teaming concept, in order that they promote trust and transparency and help end-users understand the inherent trade-offs in the optimisation process.

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Students are based at the University of Liverpool and part of the CDT and Signal Processing research community. Every PhD is part of a larger research group which is an incredibly social and creative group working together solving tough research problems. Students have 2 academic supervisors and an industrial partner who provides co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.