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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, EU Students
Funding amount: Up to £18,000 (tax free) bursary, plus fees for three years. Due to funding restrictions, all EU nationals are eligible to receive a fees-only award if they do not have "settled status" in the UK
Hours: Full Time
Placed On: 26th May 2021
Closes: 15th August 2021
Reference: SATM119

Introduction

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.

Overview

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.

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. 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.

You will be encouraged and supported in publishing your work in high-quality peer-reviewed journals. Also, you will have opportunities and supports to present your work at relevant UK and international conferences. Working with Saab Technologies UK, the research results will feed into radar classification best practices and certification to help improve the whole ecosystem from radar design to the way human operators use the system.

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. Due to funding restrictions, all EU nationals are eligible to receive a fees-only award if they do not have “settled status” 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.

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