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Automated detection and tracking of space debris using Explainable AI (Advert ref: NUDATA23/EE/CIS/KURT)

   Faculty of Engineering and Environment

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  Dr Zeyneb Kurt  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

About the Centre for Doctoral Training

This project is being offered as part of the STFC Centre for Doctoral Training in Data Intensive Science, called NUdata, which is a collaboration between Northumbria and Newcastle Universities, STFC, and a portfolio of over 40 industrial partners, including SMEs, large/multinational companies, Government and not-for profit organisations, and international humanitarian organisations. Please visit for full information.

PhD project description

Human activities and interventions in space e.g. dead satellites, launched rockets lead to accumulation of a large amount of debris in the space. It is reported that more than 30,000 of pieces of space debris that are larger than 10 cm and more than 100 million pieces of debris larger than 1 mm exist in the space. In addition to that, some natural space environment components (e.g. meteoroids in orbits) lead to a rapid rise in the level of pollution and in return perturb the sustainable space operations. Collectively, this pollution leads to a great interference to the expected/normal operation of currently active remote sensing satellites. Impact: Robust debris avoidance manoeuvres play a vital role in reducing the risk of collusion between satellites, with each other and also between satellites and idle objects. Gap: Data collection as well as detection and tracking of the trajectories of debris accurately is costly and difficult, even for ground-based facilities; which is even more challenging to achieve it within the limited space surveillance facilities.

Our first aim is to distinguish the debris-free space environment images from the ones including debris and classifying the type of debris accurately by curating a large-scale image repository. Following this step, advanced algorithms e.g. NN-aided KalmanNet, for tracking multiple classes of debris will be developed. This involves overcoming the challenge of occlusion and background clutter.


(i) To curate a large image repository of space environment including different categories of space debris; (ii) to develop debris detection models using deep neural networks, (ii) to implement tracking algorithms for multiple classes of debris that overcome the challenge of occlusion and background clutter. (vi) To explore explainable AI techniques to add explainable functionality to the deep learning models so that the decision is transparent to the users. Prediction and tracking algorithms must perform quickly during the inference time to yield accurate results. Enhancing tracking speed is especially imperative for real-time debris tracking models.

Recruitment Event

You will join a strong and supportive research team. To help better understand the aims of the CDT and to meet the PhD supervisors, we are hosting a day-long event on campus on Monday 9th January 2023.

At that event, there will be an opportunity to discuss your research ideas, meet potential PhD supervisors, as well as hear from speakers from a variety of backgrounds (academia, industry, government, charity) discussing both STFC and data science as well as their personal paths and backgrounds. Click here for details.

Eligibility Requirements:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.

Please note: to be classed as a Home student, candidates must meet the following criteria:

  • Be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they would be classed as an International student. Applicants will need to be in the UK and fully enrolled before stipend payments can commence, and be aware of the following additional costs that may be incurred, as these are not covered by the studentship.

  • Immigration Health Surcharge
  • If you need to apply for a Student Visa to enter the UK, please refer to the information on It is important that you read this information very carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application otherwise your visa may be refused.
  • Check what COVID-19 tests you need to take and the quarantine rules for travel to England
  • Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be borne by the university. Please see individual adverts for further details of the English Language requirements for the university you are applying to.

How to Apply

For further details of how to apply, entry requirements and the application form, see

Please note:

You must include the relevant advert reference/studentship code (e.g. NUDATA23/…) in your application.

If you are interested in more than one of the Northumbria-hosted NUdata research projects, then you can say this in the cover letter of your application and you can rank up to three projects you are interested in (i.e. first choice, second choice, third choice). You are strongly encouraged to do this, since some projects are more popular than others. You only need to submit one application even if you are interested in multiple projects (we recommend you submit your application to your first choice).

You do not need to submit a research proposal for the proposed project, since the project is already defined by the supervisor. If you have your own research idea and wish to pursue that, then this is also possible - please indicate this on your application (if this is the case, then please include a research proposal of approximately 300 words).

We offer all applicants full guidance on the application process and on details of the CDT. For informal enquiries, email Professor James McLaughlin ([Email Address Removed]). Please contact the Principal Supervisor of the project(s) ([Email Address Removed]) for project-specific enquiries.

Deadline for applications: 31st January 2023

Start Date: 25th September 2023

Funding Notes

The studentship supports a full stipend, paid for four years at UKRI rates (for 2022/23 full-time study this is £17,668 per year), full tuition fees and a Research Training and Support Grant (for conferences, travel, etc).


[1] Moorton Z, Kurt Z, Woo WL. Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife? Mar Pollut Bull. 2022 Aug;181:113853. doi: 10.1016/j.marpolbul.2022.113853. Epub 2022 Jul 1. PMID: 35785721.
[2] Kurt Z and Yavuz S, "A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms," 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), 2012, pp. 37-43, doi: 10.1109/INES.2012.6249866.
[3] Kurt Z and Yavuz S, "Improvement of the measurement update step of EKF-SLAM," 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), 2012, pp. 61-65, doi: 10.1109/INES.2012.6249803.
[4] Kurt Z and Yavuz S, "A simultaneous localization and map building algorithm based on sequential Monte Carlo method," 2008 IEEE 16th Signal Processing, Communication and Applications Conference, 2008, pp. 1-4, doi: 10.1109/SIU.2008.4632712.
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