Imaging, Data and Communications
All our international places for 2026 entry are now filled and we are no longer accepting applications from overseas students
Research Theme
Aim
Develop new computationally efficient algorithms for large multi-dimensional array processing.
Objectives
- Develop new fast beamforming algorithms and adaptive array processing techniques using advances in fast Fourier integral operators and randomized algorithms
- Build applications using these techniques for high-dimensional wideband sensor array processing problems, e.g. requiring the simultaneous processing of range, doppler, azimuth, elevation and frequency, in challenging acoustic and/or radar domains.
- Explore the performance trade-off between accuracy and computational efficiency.
Description
Large scale, multi-dimensional array processing problems, e.g. simultaneously processing range, doppler, azimuth, elevation and frequency, are ubiquitous in radar and sonar sensing, and it is essential to keep the computation to a minimum in order to process the outputs in a timely manner. For narrowband uniformly spaced linear arrays fast transforms such as the FFT can be exploited. However, dealing with large wideband non-uniform arrays remains a major challenge.
This project will develop novel fast array processing algorithms for statistical estimation and beamforming based on two recent developments in signal processing and related mathematics. The first is the construction of fast Fourier integral operator approximations [1,2,3,4], that can provably approximate the Fourier integral to a given level of accuracy in O(n.log(n)) while accommodating different sampling/physical geometries. The second is efficient fast randomized algorithms for solving large least square problems [5,6] that offer the potential for novel beamforming solutions [7]. The project will explore how these ideas can be integrated to develop novel efficient array processing solutions to tackle challenging sonar and RF array processing problems and to assess their performance trade-off between accuracy and computation.
[1] E. Candes, L. Demanet, & L. Ying, 2007, Fast Computation of Fourier Integral Operators. SIAM Journal on Scientific Computing, Vol. 29, Iss. 6, pp. 2464-2493.
[2] L. Demanet, M. Ferrara, N. Maxwell, J. Poulson, and L. Ying, 2012, A butterfly algorithm for synthetic aperture radar imaging. SIAM J. Imag. Sci., vol. 5, no. 1, pp. 203–243.
[3] S. I. Kelly and M. E. Davies, 2014, A fast decimation-in-image back-projection algorithm for SAR. 2014 IEEE Radar Conference, pp. 1046-1051.
[4] S. I. Kelly, M. E. Davies, J. S. Thompson, 2014, Parallel Processing of the Fast Decimation-in-image Back-projection Algorithm for SAR. 2014 Sensor Signal Procesing for Defence (SSPD), pp. 1-5.
[5] V. Rokhlin and M. Tygert, 2008, A fast randomized algorithm for overdetermined linear least-squares regression. Proceedings of the National Academy of Sciences, 105(36), pp 13212–13217.
[6] P.-G. Martinsson and J. A. Tropp, 2020, Randomized numerical linear algebra: Foundations and algorithms. Acta Numerica, 29, 403–572.
[7] R. S. Srinivasa, M. A. Davenport and J. Romberg, 2019, Trading Beams for Bandwidth: Imaging with Randomized Beamforming. SIAM J. Imag. Sci., vol. 13, no. 1, pp. 317-350.
Industry Partner
This project is co-funded by Thales UK.
Applications
First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.
This project requires a student with a high level of mathematics or numeracy.
Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position.
Research Theme
Aim
To develop machine learning algorithms for the robust detection and classification of natural and man-made objects in the ocean environment.
Objectives
- Leverage unsupervised learning to understand the background ocean environment as a reference for detecting objects.
- Develop a few-shot learning approach to classify the acoustic signals emitted by marine mammals and vessels travelling on the surface of the sea.
- Explore strategies for improving the robustness of underwater acoustic models to distribution shifts.
Description
Passive sonar is a key sensing modality for achieving understanding of the ocean environment, to help protect naval platforms. This technology can be applied in a range of systems, from hull-mounted or towed hydrophone arrays to sonobuoys or uncrewed underwater vehicles. Sonar contact classification is a challenge, due to the need to detect, label, and track multiple targets. Much of this work is currently carried out manually by sonar operators. The growing risk from increasingly stealthy targets, complex environments, and a data deluge from more capable sensors with more channels, necessitates new automatic approaches to marine object detection and localization.
Recent developments in the fields of artificial intelligence (AI) and machine learning (ML) offer promise for improvements in the analysis of acoustic signals, in terms of speed, accuracy, and robustness. This project will investigate novel approaches to analyzing the data and provide an increased understanding of the maritime arena.
In later years of the PhD, QinetiQ would like to investigate the possibility of an internship for the student for up to 6 months.
Applications
Applications for entry in session 2027 will open later in the year.
QinetiQ has developed an algorithm for detecting the time stamps of sound cuts of short mammal calls in the master tapes of the Watkins Marine Mammal Sound Database. This enables the development and assessment of object detection algorithms as opposed to just classification of sound cuts, which is what the database is usually used for. The algorithm can be made available to the student to use and improve on.
QinetiQ has access to unclassified recordings of surface vessels. Subject to permission of the data owner, these could be made available to the student to support the research.
QinetiQ: https://www.qinetiq.com
Watkins: https://whoicf2.whoi.edu/science/B/whalesounds/index.cfm
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.
Further information on English language requirements for EU/Overseas applicants.
Full funding is available for this position.