In naval operations, it is necessary to optimally deploy and position a finite number of sonar sensing vehicles (manned and autonomous) to maximise their combined detection coverage over a geographical area of interest. Performance prediction of the sonar systems is crucial. Currently, performance prediction is complicated and user-intensive. It involves environmental data gathering, acoustic propagation modelling, sonar system modelling, and manual interpretation of the modelled outputs. Environmental data can be highly variable in space and time and is obtained from different sources (direct in-situ measurements, numerical models, and historical databases). The underwater acoustic models predict how sound propagates between sources and receivers, which depends upon the environmental data as well as the source and receiver characteristics (e.g., frequency and depth). The sonar performance models predict detection performance from the acoustic characteristics output from the acoustic models and the sonar characteristics (e.g. aperture size and types of signal processing applied to the acoustic signals). Users interpret the outputs of the performance models and use these to develop plans for the deployment of naval assets. This PhD aims to incorporate AI into the chain to aid and enhance decision-making for optimal deployment. We envisage that with exposure to a sufficiently comprehensive example dataset (i.e., training), optimal deployment could be achieved directly from data earlier in the chain, ultimately directly from key environmental features. Such features might include the spatial/temporal distribution of the oceanic mixed layer depth or the seabed type for shallow water areas, both of which have a significant effect on acoustic propagation and therefore sonar performance. Incorporating AI within a naval decision chain will require a high degree of transparency. This is crucial for developing trust with operators, for accountability and for responsible use.
My research has a key focus on its transparency due to the nature of its end users, therefore the explainability of AI and identifying where accountability should lie when using AI algorithms is of great interest to me.
Other areas include:
AI for practical decision making.
Bayesian machine learning.
Responsible AI use within the defence sector.
MSci in Geophysics at Imperial College
I worked at a world leading Marine Robotics company for a year specialising in data analytics.
Dr Alan Hunter
Marcus Donnelly (SEA Ltd)