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Artificial Intelligence Tactical Decision Aid for Management of Naval Sensors and Autonomous Vehicles


Project Description

In naval tactical operations, it is necessary to optimally deploy and position a finite number of vehicles (manned and autonomous) in order to maximise the combined detection coverage of their sonar sensing systems over a geographical area of interest. Performance prediction of the sonar systems is crucial to this process.

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. The 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). Expert users interpret the outputs of the sonar performance models and use these to develop operational plans for the deployment of the naval assets.

This PhD aims to incorporate artificial intelligence into the chain to aid and enhance decision-making for optimal deployment of the naval assets. It is envisaged that with exposure to a sufficiently comprehensive example dataset (i.e., training), optimal deployment of assets could be achieved directly from data increasingly early 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. The work will use simulated data (or the necessary software for generating it) supplied by the industry partner (SEA).

Clearly, incorporating AI within a naval tactical decision-making chain will require a high degree of transparency. This is crucial for developing trust with naval operators and for accountability and responsible use.

This project is associated with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its second cohort of at least 10 students to start in September 2020. Further details can be found at: http://www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Informal enquiries about the project should be directed to Dr Alan Hunter on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002

Start date: 28 September 2020.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 61.00

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

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