AI-based framework for drone intent classification using a non-cooperative radar system PhD

This is an exciting opportunity for a fully-funded PhD studentship in the Centre for Autonomous and Cyber-Physical Systems at Cranfield University, to develop AI-based drone intent classification methods to improve radar conflict detection and alerting for safety in a blended airspace. This PhD investigates and develops Deep Learning (DL) methods for classifying the intent of drones based on non-cooperative radar signatures in order to detect of conflicts and their resolution. 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. Read moreRead less

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 an AI-based drone intent classifier that is based on non-cooperative radar signatures to improve conflict detection and alerting, as an automated way to improve safety in a blended airspace, whilst meeting the false alerts rate requirements of manned aviation. The use of Deep Learning (DL) techniques in the intent classification of drones to detect potential conflicts 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.

This PhD will be hosted by the Centre for Autonomous and Cyber-Physical Systems and will be based at DARTeC which is a £67 million new research centre that will enable “Aviation of the Future”. Cranfield is also setting up 16 km long national facility for drone flights (referred to as drone corridor) which will be extensively used for experimentation in this project. The Centre for Autonomous and Cyber-Physical Systems is one of the world’s largest centres of postgraduate education and research, with over 200 MSc and PhD students. In terms of facility, Cranfield University has a range of specialist research facilities available for different research activities (e.g. MUEAVI-multi-user environment for autonomous vehicle innovation facility). The facility operates as a collaborative and flexible space with specialist equipment available for indoor/outdoor flight tests for UAS systems. Also, the Centre for Autonomous and Cyber-Physical Systems offers the environment for algorithm development and simulation. Also, the centre can offer support, assistance with analysis, and method development for research.

The PhD will demonstrate 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 own 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.

This is a very exciting project for a suitable candidate where you will be exposed to latest technological developments, learn from the industrial and academic experts working in this area and prepare for an exciting career in academia or industry.

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 hands-on approach to systems integration and out of the box thinking ability.