PhD in Advanced Electronic Surveillance DSP and ML Techniques
PhD in Advanced Electronic Surveillance DSP and ML Techniques
Prof Matt Ritchie
Applications accepted all year round
Funded PhD Project (UK Students Only)
About the Project
We are looking for a motivated PhD student to work in the areas of Radio Frequency (RF) sensing, Machine Learning and Digital Signal Processing (DSP)
Title: Multi-channel and Multistatic RF sensing of signals
Duration: 4 years
Project supervisor:Professor Matthew Ritchie
Department: Electronic and Electrical Engineering, University College London
Start Date: Oct, 2026
Closing Date:None (the position will remain open until a suitable candidate is found)
OPEN TO UK NATIONALS ONLY
How to apply: Applications must be made using the UCL online application system by using the UCL postgraduate study application form. Please mark it to the attention of Dr. Matthew Ritchie.
Please submit your CV, transcripts (both undergraduate and postgraduate), your latest thesis (and/or one of your publications if applicable), and a short (up to one page) cover letter explaining why you think you are a suitable candidate for this post. The short-listed candidates might be further required to provide contact info (no direct recommendation letters) for peers that can recommend them.
Description:
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.
What is covered by this studentship: 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.
For inquiries about the position, please contact Professor Matthew Ritchie m.ritchie@ucl.ac.uk.
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