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Using machine learning and artificial intelligence to improve the tracking of vessels in sonar spectrograms (Distributed Algorithms CDT)


   Department of Electrical Engineering and Electronics

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  Prof S Maskell  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

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

The successful PhD student will be co-supervised by Professor Simon Maskell and will work alongside our external partner Ultra

The studentship is open to: British and foreign nationals who can obtain HMG Baseline Personnel Security Standard (BPSS)

PROJECT DESCRIPTION

Hydrophone sonar data can be used to determine the current and previous locations of nearby vessels, as well as to estimate where they may be heading. Trained human operators currently analyse the data in the form of broadband waterfall displays to detect and track the vessels. Although this is time-consuming and expensive, these human operators currently outperform traditional automated passive contact follower algorithms, such as the Kalman and Alpha-Beta filters: these filters are susceptible to the abundant underwater noise and struggle with crossing tracks and quiet contacts, while humans can use their experience to learn how to mitigate the challenging aspects of the task. An automatic detection and tracking model that is more accurate and robust than traditional methods would reduce the human operator’s workload so that the human operators only need to investigate contacts of interest. It is also crucial that this model can detect the quieter contacts, which are of most interest to the navy, but do so without increasing the false alarm rate.

Although machine learning and artificial intelligence approaches seem ideal for this task, there are challenges. For example, the model must perform in real-time: the waterfall display is updated line-by-line each second. Whilst the model can use the previous rows of data, it must solely rely on the time vs. bearing (waterfall) data. The model may also need to be trained on large volumes of synthetic data due to the lack of real and unclassified data, and thus distributed training and techniques such as transfer learning may be required to increase the accuracy on what little real data exists.

The development of automatic detection and tracking algorithms are currently a hot topic in the data science and machine learning sectors due to the challenges involved in developing techniques that can outperform existing methods and the wide range of applications of the techniques in industry. This project would enable you to become an expert in a highly relevant and sought-after discipline, as well as engaging and gaining experience with an industry partner in the fascinating field of defence.

The expected outcome of this project is an automatic detection and tracking algorithm for broadband waterfall displays that will enable the human operators to only need to focus on contacts of interest thus reducing their workload. This model must perform in real-time as each new instance of data is recorded. The approaches developed will need to outperform techniques (such as those based on Kalman filters and Alpha-Beta filters) that have been configured, over decades, to work well in these contexts.

A previous feasibility study undertaken in collaboration with the University of Liverpool has shown promising results for Long Short-Term Memory (LSTM) networks. LSTMs can retain useful information about the previous time steps and exploit this when processing the current data. These models need to be adapted to work effectively with the broadband sonar data to efficiently produce tracks that are less susceptible to transient noise, more stable and can, potentially in combination with post-processing, correctly resolve crossing contacts.

This project is due to commence on 1 October 2022.

Students will be based at the University of Liverpool and will be part of the CDT and Signal Processing  research community - a large, social and creative research group that works together solving tough research problems. Students are supervised by two academic supervisors and an industrial partner who provides project direction, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain the skills needed to work at the interface of academia and industry.

The CDT is committed to providing an inclusive environment in which diverse students can thrive and particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector. We can also consider part time PhD students. We also encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference.


Funding Notes

Visit the CDT website for funding and eligibility information.
You must enter the following information:
• Admission Term: 2022-23
• Application Type: Research Degree (MPhil/PhD/MD) – Full time
• Programme of Study: Electrical Engineering and Electronics – Doctor in Philosophy (PhD)
The remainder of the guidance is found in the CDT application instructions on our website - https://www.liverpool.ac.uk/distributed-algorithms-cdt/apply/.
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