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Deep Learning Techniques for Missiles Seekers Automatic Target Recognition (ATR)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Full Studentship including Industrial Placement, Duration: 3 years

With our esteemed partner, we are looking for a well-qualified and motivated PhD student to conduct the following fascinating research project:

Cranfield University and MBDA have defined a program of research entitled "Deep Learning Techniques for Missiles Seekers ATR". The PhD has the ambition to tackle the fundamental problem of providing a reliable and efficient mean of automatic object/target recognition using up to date machine learning techniques such as Deep Convolutional Neural Networks and other Deep Q Learning techniques in vey challenging Defence application scenarios contributed by our prestigious and leader in the world partner, MBDA, as part of a new and innovative research program.

The proposed development may be useful in strengthening the tracking capabilities in the presence of countermeasures and clutter. We will assess the benefit of this new era of machine learning techniques in terms of recognition accuracy, computational load and their off-line and online learning requirements. We are looking at further implementation within the image processing and guidance framework used at MBDA.

The latest laser based technology providing 3D information about the scene could be very useful to conduct the automatic recognition task and be more robust against diversion. Very recent studies showed that this 3D modality is a plus to have although some limitations due to the resolution available of this sensing modality and the reduced number of processing tools for this kind of data are still limiting its full exploitation. Deep Learning strategies would be extended to deal with this kind of data modality.

Entry Requirements

Applicants must be UK nationals or EU only. Applicants should hold at least a Bachelor Degree of either First Class or hold a Masters Degree (MSc/MRes) in Electrical/Computer Science/Applied Mathematics or any other relevant discipline.

Candidates should have a good basis in mathematics with good scientific programming skills (Matlab, C++). Experience in Computer Vision and Machine Learning is an important asset. Good interpersonal and communication (oral and written in English) skills are also required.

How to apply

A CV and the name and address of two referees should be sent to Prof Aouf at .

Funding Notes

Applicants are eligible for a bursary of up to £15,000-£16,000 p.a. for the duration of the award dependent upon qualifications and experience. This studentship will additionally cover the tuition fees for qualified UK/EU

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