Professor Simon Maskell, from the University of Liverpool, has been announced as the new Defence Science and Technology Laboratory (Dstl) and Royal Academy of Engineering Research Chair in Information Fusion.
Through this new Chair, Professor Maskell will focus on developing next generation and generation-after-next algorithms with applications in real-world intelligence, security and defence situations.
He will look to develop long term, strategic and sophisticated algorithmic solutions and add them to Stone Soup, an open-source software framework developed by Dstl to help improve tracking technology.
Professor Maskell’s new Research Chair fulfils the need for a longer-term strategic perspective on how algorithms and models can better support intelligence analysis and improve decision-making.
More specifically, it will ensure the algorithms used in Stone Soup are able to cater for deception, are robust against attack and are able to deduce the best short-term decisions for meeting long-term information goals.
Professor Maskell said: “I am delighted to be awarded this new Chair which builds on my work with Dstl over many years. The development and application of these new algorithms are vitally important in finding and assessing potential threats and ensuring decision-makers are given the best information to respond to defence’s challenges.”
The Royal Academy of Engineering Research Chairs aim to strengthen the links between industry and academia and provide support towards collaborative research projects between the awardee, their industrial sponsor, and their host institution.
AUKUS Innovation Challenge
In addition, Professor Maskell will develop solutions in electromagnetic surveillance thanks to funding provided through the UK’s Defence and Security Accelerator (DASA).
The funding is in support of the AUKUS partnership, a landmark trilateral security agreement between the United Kingdom, the United States of America and Australia.
Professor Maskell’s project will use a combination of machine learning and statistics to improve the ability to detect multiple individual faint signals in close geometric proximity to one another.
It is one of only four successful projects to receive research funding through the inaugural AUKUS Innovation Challenge programme.