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Doctor of Philosophy (Australian National University), Bachelor of Engineering (Australian National University), Bachelor of Science (Australian National University)
Jason is an Associate Professor in Electrical Engineering at Queensland University of Technology (QUT). He graduated from the Australian National University with the B.Sc. and B.E. degrees from the Australian National University in 1995, and graduated with a PhD degree from the Australian National University in 1998. In 1998 Jason joined 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 University of New South Wales at the Australian Defence Force Academy. In 2005 Jason joined the Queensland University of Technology as a research fellow, before appointment as Lecturer in Electrical Engineering at the Queensland University of Technology in 2007 (promoted to Senior Lecturer in 2010 and Associate Professor in 2016).
Jason’s research expertise is the areas of non linear filtering, estimation, system identification, detection and control for robotic and dynamic systems. Current research interests include platform autonomy for infrastructure inspection and low signal-to-noise ratio anomalous signal detection.
Recent research impact highlights include:
- Development of automation systems for infrastructure inspection aircraft within the ROAMES asset management system.
- Development of vision based sense and avoid technology for unmanned aerial systems. A recent summary can be found in a feature article in IEEE Aerospace and Electronic System Magazine.
Publications Please see Jason’s Google Scholar page for a list of publications and their citations
- Google Scholar: https://scholar.google.com.au/citations?user=vy-McVsAAAAJ&hl=en
Patents
- Method and apparatus for developing a flight path. Inventors: Troy Bruggemann and Jason Ford, Publication date 2015/6/11, International Application No. PCT/AU2014/050360
Projects
Additional information
- 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
- Molloy TL, Ford JJ, (2018) Minimax robust quickest change detection in systems and signals with unknown transients, IEEE Transactions on Automatic Control p1-8
- 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
- James JF, Ford JJ, Molloy TL, (2017) Change detection for undermodelled processes using mismatched hidden Markov model test filters, IEEE Control Systems Letters p238-243
- 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
- Title
- Automated vision-based aircraft collision warning technologies
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- LP100100302
- Start year
- 2010
- Keywords
- Collision Warning; Aerial robotics; National Airspace
- Advanced Detection Techniques for Enhanced Vision-Based Sense and Avoid
PhD, Principal Supervisor
Other supervisors: Dr Timothy Molloy, Professor Tristan Perez - Nonlinear Disturbance Observers with Applications to Adaptive Wave Filtering, Detection and Ship Ride Control
PhD, Associate Supervisor
Other supervisors: Professor Tristan Perez - Identification of Non-linear Dynamical Systems with Application to Agricultural Machinery
PhD, Associate Supervisor
Other supervisors: Professor Tristan Perez - Bayesian risk analysis of autonomous intelligent agents an networks
PhD, Associate Supervisor
Other supervisors: Professor Tristan Perez
- 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)