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PhD Studentship - Robust adaptive framework for collision detection and alerting using a non-cooperative radar system PhD

Cranfield University - Centre for Autonomous and Cyber-Physical Systems

Qualification Type: PhD
Location: Cranfield
Funding for: UK Students
Funding amount: Up to £18,000 studentship, plus fees* for three years
Hours: Full Time
Placed On: 8th October 2021
Closes: 15th December 2021

Application deadline - 15 Dec 2021

Award type(s) - PhD

Start date - 31 Jan 2022

Duration of award - 3 years

Eligibility - UK

This is an exciting opportunity for a fully-funded PhD studentship in the Centre for Autonomous and Cyber-Physical Systems at Cranfield University, in the field of conflict detection and alerting for safety in a blended airspace. This PhD investigates and develops Metaheuristics and Deep Learning (DL) methods for the detection of potential conflicts and their resolution based on non-cooperative radar signatures and their classification. This research is sponsored by EPSRC and SAAB UK under the Doctoral Training Partnership Funding 2020/21. The studentship will provide a bursary of up to £18,000 (tax-free) plus fees* for three years.

Artificial Intelligence (AI) in civilian Air Traffic Management (ATM) is still in its infancy. With the proliferation of Unmanned Autonomous Vehicles (UAV) applications (e.g. surveying, medical deliveries etc), systems and services to allow them to co-exist with manned aviation and to be used within controlled airspace are being developed. To ensure safety, it is paramount that aviation users and operators are alerted of potential conflicts between aircraft and between aircraft and UAVs as well.

This PhD proposes to develop a robust conflict detection and alerting mechanism that is based on non-cooperative radar signatures and their classification, as an automated way to improve safety in blended airspace, whilst meeting the false alerts rate requirements of manned aviation. The use of Metaheuristics and Deep Learning (DL) techniques in the detection of potential conflicts and their resolution will be investigated, in order to improve the conventional classification and conflict detection algorithms used. The solution will significantly enhance the capabilities of existing non-cooperative radar systems in manned aviation, as well as counter UAV radar systems, enhancing the safety and security of the blended airspace.

Cranfield is an exclusively postgraduate university that is a global leader for education and transformational research in technology and management. It is the only University that has its own commercial Airport, controllers, commercial pilots and aircraft. Cranfield Airport was the first to install and operate a Digital Tower in the UK, supplied by SAAB the PhD industrial sponsor.

The PhD will demonstrate how Metaheuristics and Deep Learning (DL) methods for detection of potential conflicts and their resolution can be applied in order to improve the conventional classification and conflict detection algorithms used.

Entry requirements

Applicants must have a first or second-class degree, and a Master’s degree, in engineering or a related informatics or computer science area. This project would suit someone with a strong background in computer programming, signal/image-processing (e.g. classification algorithms) and a hands-on approach to systems integration and out of the box thinking ability.

Funding

To be eligible for this funding in full, applicants must be a UK national or have a permanent residence in the UK.

About the sponsor

Sponsored by EPSRC, Cranfield University and SAAB UK, this studentship will provide a bursary of up to £18,000 (tax free) plus fees* for three years.  

How to apply

If you are eligible to apply for this studentship, please complete the online application form.

https://www.cranfield.ac.uk/research/phd/robust-adaptive-framework-for-collision-detection-and-alerting-phd/?utm_channel=&utm_source=warwickuniversity&utm_medium=listing&utm_campaign=SATM_Aero_AK_PhD_SATM236_PhD_jobs_Oct21

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