PhD in Advanced Electronic Surveillance DSP and ML Techniques

Duration of study: Full time - 4 years fixed term
Application deadline: No closing date, the position will remain open until a suitable candidate is found.

Primary Supervisor: Professor Matthew Ritchie

Project Description: Multi-channel and Multistatic RF sensing of signals

The electromagnetic environment (EME) is becoming increasingly congested and contested. Designers of both radar and communications systems are developing methods that are both more complicated and increasingly harder to detect. Electronic Surveillance (ES) is therefore facing an increasingly complex environment to operate within. As radar and communications systems develop in their adaptability the requirements on ES systems increase significantly.

Recently there have been attempts to bring the advances made in machine learning (ML) to the understanding of the EME. Several works have applied ML to modulation recognition in communications whilst others have attempted to classify individual radar transmitters from their radio frequency emissions (RF). ES could leverage these techniques to provide the operator with greater understanding of the complicated EME around them. Little academic research has been completed on real world applied problems considering low SNR, congested and contested environments.

Many open research questions exist in the application of ML to the Electromagnetic Environment (EME). These include:

• Array-based processing for ES: How can you use different array configurations to enhance direction of arrival estimation in combination with signal parameter estimation.
• Using multiple ES receivers: Combine data to classify the signals that are present in a complex EM environment.
• Focus on edge ML methods on ES data: Develop methods that allow for deployment on low Size Weight and Power (SWaP) hardware solutions

These questions are present across many of the problems within ES that ML could address. The PhD will therefore focus on a particular research area, detecting, counting and separating signals in a congested EME using ML.

The PhD will include elements of simulation, algorithm design and hardware implementations. The balance of this will be discussed during the research project depending on skill sets of the PhD and feedback from the sponsor.

The hardware opportunities include the use of cutting-edge equipment such as the Xilinx RFSoC system the AIR-T SDR, BladeRF SDR, LimeSDR and many more. These devices can be used to create datasets in order to validate newly generated ML techniques on real experimental data.

As part of the ICASE PhD the student can engage closely with the sponsor Dstl over the 4-year period. Dstl has a team of experts in the area of RF sensing and ES that will provide technical partnering on this research. As part of this ICASE award the PhD student will be able to take secondments at the Dstl site and work closely with expert engineers within this research topic. It is a strict requirement that the applicant must be a UK national, due to security requirements for on-site working with the sponsor.

Funding: A stipend (currently £26,305 pa) and fees at the home rate (currently £6,400 pa) for a period of 4 years. This studentship also covers the cost of hardware, consumables and travel expenses to allow attendance to conferences during the PhD. More details about the stipend and fees can be found here: https://www.ucl.ac.uk/research-innovation-services/studentship-budgets-202122

Please note that this PhD is open to UK nationals only due to restrictions from the sponsor.

Person specification: The candidate should meet the entry requirements detailed here: https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/electronic-and-electrical-engineering-mphil-phd for PhD programmes at UCL EEE.

They should have at least an upper second-class honours degree (2:1 or equivalent qualification) in Electrical Engineering, Computer Science, Applied Mathematics, or related fields.

Additional requirements include an outstanding academic record and strong Digital Signal Processing background.

Experience with research is not required but is a plus. Additional desirable skills include prior knowledge in Radar sensing, RF design, and machine learning.

If you fit these specifications, like challenging tasks, and are passionate about research, then we would love to hear from you.

How to apply: For inquiries about the position, please contact Professor Matthew Ritchie m.ritchie@ucl.ac.uk.

The Department of Electronic and Electrical Engineering at UCL was established by Professor Sir Ambrose Fleming in 1885 and has a very strong research culture, state-of-the-art research equipment and facilities, and a very rich history of many fundamental research achievements in electronic and electrical engineering. The department has received top ratings in every UK research evaluation carried out to date. Further information regarding UCL may be found at www.ucl.ac.uk/. Information about the departments may be found at: www.ucl.ac.uk/eee.