Contact Dr Dimitrios Panagiotakopoulos
Areas of expertise
- Aeronautical Systems
- Aerospace Structures
- Autonomous Systems
- Aviation Management & Operations
- Electric and Hybrid Vehicles
- Flight Physics
- Space Systems
- Vehicle Aerodynamics
- Vehicle Engineering & Mobility
With over 15 years of contributions to the state-of-the-art in Air Traffic Management (ATM), currently enabling the path towards its digitalisation, Dimitri now heads the Unmanned Aircraft Systems Traffic Management (UTM) Research group at Cranfield University.
Before joining Cranfield, Dimitri worked as an ATM Systems Specialist at Air Navigation Solutions at Gatwick Airport, where he led the development of the Airport's 10-year Capacity Enhancement Roadmap to optimise and improve operations and infrastructure to enable Air Traffic Movement growth, which identified Operational Improvements from SESAR and NextGen Programmes and coordinated the different Airport stakeholders, including Airlines, Ground Handlers, Air Traffic Control to implement these solutions. During that time, Dimitri delivered a number of ATM systems to enable Airport operations and capacity growth, including an extension to its multi-lateration system, to allow a new Boeing Hangar (fitting 2 Code F aircraft) to be built without affecting operations.
Previous to this, Dimitri was a Senior Project Systems Engineer at NATS (formerly
National Air Traffic Services), where he single-handedly delivered complex
multi-million pounds Surveillance solutions to Airports, on time and on budget. This included the very first use of an X-band radar for
windfarm mitigation in the UK.
Dimitri holds a PhD from Imperial College London on Satellite Navigation
and Positioning Systems, with a thesis that developed innovative statistical
algorithms to monitor and guarantee the integrity of GNSS-based position and
navigation computations within safety-critical aircraft flight management
systems. It was the first time Extreme Value Theory was introduced in an
Our Research Group is interested in leveraging emerging technological advances such as Machine Learning, data-driven solutions using smart contracts and distributed ledger technology, to enable more collaborative, efficient and optimised integrated ATM/UTM operations.
There are always opporutnities for self-funded PhDs.
Contact us to find out more for funded PhD around our current and future activities.
We are currently setting up the National Beyond Visual Line of Sight (BVLOS) Experimental Corridor (NBEC) within and around the Cranfield Airport Airspace to allow us to test and validate new Concepts, systems, procedures that enable BVLOS drone flights in non-segregated airspace. This together with our high-end simulator facilities and digital twins provides a unique live/virtual sandbox and UTM/UAM ecosystem to address the challenges that ATM/UTM integration and UAM applications present.
On-going areas of interest include:
- applying deep learning and explainable AI methods in Air Traffic Management and safety-critical applications
- intelligent operation-centric and risk-minimising UAS flight / contigency / emergency planning and management, de-confliction, collision avoidance solutions development
- data-driven Comms, Navigation and Surveillance (CNS) signal and performance analysis to derive global quality of service in rural, semi-urban and urban contexts and ascertain optimum CNS combination for specific mission
- alternative navigation mode mechanism that leverage information captured by the UAS on-board capabilities (e.g. camera, inertial navigation system (INS))
- increased automation of ATM/UTM interface, including interaction with Air Traffic Control
- data driven approach to Urban Air Mobility mission planning, traffic flow and capacity management.
Boeing, Airbus, SAAB, Thales,
Articles In Journals
- Panagiotakopoulos D, Majumdar A & Ochieng WY (2014) Extreme value theory-based integrity monitoring of global navigation satellite systems, GPS Solutions, 18 (1) 133-145.
- Panagiotakopoulos D, Majumdar A & Ochieng W (2009) Characterizing the distribution of safety occurrences in aviation: an approach using extreme value theory, Transportation Research Record, 2106 (2106) 129-140.