- Professor Jason Ford
- Professor in Electrical Engineering
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Robotics and Autonomous Systems
- Discipline *
- Electrical and Electronic Engineering, Aerospace Engineering
- +61 7 3138 2207
- View location details (QUT staff and student access only)
- Identifiers and profiles
Doctor of Philosophy (Australian National University), Bachelor of Engineering (Australian National University), Bachelor of Science (Australian National University)
- Professional memberships
Infrastructure Inspection, Quickest change detection, Inverse optimal control, Estimation theory, Platform control
Jason creates decision systems for dynamic systems, including robotic and autonomous systems, that can reliably operate in the presence of uncertainty and error. Jason has over 20 years of experience developing solutions for energy, aerospace and defence industries. He has led the development of several aerial autonomous system technologies including general aviation aircraft flight control systems which are operating in Australian, European and US airspaces.
Jason’s technical expertise is in the area of information-theoretic and optimisation approaches to model-based filtering, estimation, detection, decision and planning for dynamic systems and their related inverse problems. He uses this expertise to create reliable autonomy solutions in aerospace, robotics and power systems. His current research interests include aerial platform autonomy for infrastructure inspection and low signal-to-noise ratio anomalous signal detection with application in aerospace and other domains.
Current Research Projects:
Jason is currently looking for PhD students in these two areas:
- Wide field-of-view vision based aircraft detection.
- Advanced statistical based quickest change detection technology.
Academic and Professional Experience:
Dr Jason J. Ford is a Professor at the Queensland University of Technology (QUT). He graduated from the Australian National University (ANU) with the B.Sc. and B.E. degrees in 1995, and a PhD degree in 1998. In 1998 Jason joined the Australian Defence Science and Technology Organisation (now called DSTG) as a Research Scientist (promoted to Senior Research Scientist in 2000). In 2004 Jason was appointed a Research Fellow at the University of New South Wales at the Australian Defence Force Academy. In 2005 Jason joined the QUT as a Research Fellow, before appointment as Lecturer in Electrical Engineering in 2007 (promoted to Senior Lecturer in 2010, to Associate Professor in 2016 and to full Professor in 2019).
Research and Industry Impact Highlights:
- Development and commercialisation of aircraft automation systems for infrastructure inspection within the ROAMES asset management system. Savings to the state of Queensland (alone) are estimated to exceed $40M/year, but ROAMES also operates in the US and UK. The impact of this rare example of an Australian developed aircraft flight technology has been acknowledged via:
- the Australian Research Council’s 2018 Engagement and Impact exercise assessed the aircraft flight technology as having high impact (their highest rating).
- the ROAMES system winning a UK Network Game changer Award, a US International Edison Award, a Queensland Spatial Excellence Awards, JM (Mac) Seriser Award.
- More than a decade of sustained research activity on the extremely challenging problem of replicating the human pilot vision system to create a vision based sense and avoid technology for autonomous aerial systems. The impact of this technology has been acknowledged via collaboration project awards:
- a Queensland iAward,
- a B-HERT award, and
- an Engineering Australia Excellence Award (Queensland Division).
- Co-authored more than 100 peer reviewed research publications: Google Scholar, QUT E-prints, ORCID, and/or Publons.
- Patented flight plan and flight control technology: Method and apparatus for developing a flight path. Inventors: Troy Bruggemann and Jason Ford, Patent details can be found via the following patent numbers – Australia: 2014360672, United States: 9983584, Canada: CA2969552, Europe: EP3077881.
- Attracted over $10 million dollars in competitive research funds within Australia since 2009.
- A named Associate Investigator in the ARC Centre of Excellence in Robotic Vision.
- 6 PhD completions as principal supervisor since 2009.
- Taught Control System Engineering and Autonomous Systems to more than 1000 undergraduate electrical, aerospace and mechatronic engineers.
Teaching Areas: Electrical engineering.
- Molloy TL, Inga J, Flad M, Ford JJ, Perez T, Hohmann S, (2019) Inverse open-loop noncooperative differential games and inverse optimal control, IEEE Transactions on Automatic Control p1-8
- Molloy TL, Ford JJ, (2019) Minimax robust quickest change detection in systems and signals with unknown transients, IEEE Transactions on Automatic Control p2976-2982
- Molloy TL, Ford JJ, Perez T, (2018) Finite-horizon inverse optimal control for discrete-time nonlinear systems, Automatica p442-446
- James JF, Ford JJ, Molloy TL, (2018) Learning to detect aircraft for long range, vision-based sense and avoid systems, IEEE Robotics and Automation Letters p4383-4390
- James JF, Ford JJ, Molloy TL, (2018) Quickest detection of intermittent signals with application to vision-based aircraft detection, IEEE Transactions on Control Systems Technology p1-8
- Molloy TL, Ford JJ, Mejias Alvarez LO, (2017) Detection of aircraft below the horizon for vision-based detect and avoid in unmanned aircraft systems, Journal of Field Robotics p1378-1391
- Molloy T, Ford JJ, (2016) Asymptotic minimax robust quickest change detection for dependent stochastic processes with parametric uncertainty, IEEE Transactions on Information Theory p6594-6608
- Lai JS, Ford JJ, Mejias Alvarez LO, O'Shea PJ, (2013) Characterization of sky-region morphological-temporal airborne collision detection, Journal of Field Robotics p171-193
- Lai J, Mejias Alvarez LO, Ford JJ, (2011) Airborne vision-based collision-detection system, Journal of Field Robotics p137-157
- Bruggemann TS, Ford JJ, Walker RA, (2011) Control of aircraft for inspection of linear infrastructure, IEEE Transactions on Control Systems Technology p1397-1409
For more publications by this staff member, visit QUT ePrints, the University's research repository.
Grants and projects (Category 1: Australian Competitive Grants only)
- Automated vision-based aircraft collision warning technologies
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- Start year
- Collision Warning; Aerial robotics; National Airspace
- Identification of Non-linear Dynamical Systems with Application to Agricultural Machinery
PhD, Principal Supervisor
Other supervisors: Associate Professor James McGree, Dr Timothy Molloy
- Nonlinear Control of Interconnected Multi-Agent Systems Subject to Disturbances and Measurement Errors
PhD, Principal Supervisor
Other supervisors: Dr Alejandro Donaire, Dr Aaron Mcfadyen
- Quickly Detecting Aircraft in Image Sequences
PhD, Principal Supervisor
Other supervisors: Dr Timothy Molloy
- Collision Risk Modelling for Unmanned Aircraft Separation and Traffic Management
PhD, Associate Supervisor
Other supervisors: Dr Aaron Mcfadyen
- Nonlinear Disturbance Observers with Applications to Adaptive Wave Filtering, Detection and Ship Ride Control
PhD, Principal Supervisor
Other supervisors: Adjunct Professor Francis Valentinis, Dr Alejandro Donaire, Dr Christina Kazantzidou
Completed supervisions (Doctorate)
- Online Hidden Markov Model Parameter Estimation and Minimax Robust Quickest Change Detection in Uncertain Stochastic Processes (2015)
- Filter and control Performance Bounds in the Presence of Model Uncertainties with Aerospace Applications (2013)
- Visual Guidance for Fixed-Wing Unmanned Aerial Vehicles Using Feature Tracking: Application to Power Line Inspection (2013)
- A Hidden Markov Model and Relative Entropy Rate approach to Vision-based Dim Target Detection for UAV Sense-and-Avoid (2010)
- Robust Adaptive Control of Rigid Spacecraft Attitude Maneuvers (2008)