Damien Jourdan
Papers
1
Total Citations
2
H-Index
1
About
Damien Jourdan is a researcher advancing the safety and autonomy of fixed-wing aerial vehicles through novel sensor fusion and learning-based control. His key research areas include aerodynamic state estimation, air data systems, and neural-network-enhanced filtering for high-speed flight. Jourdan’s most notable contribution is a learning-based air data system that enables aerial robots to operate safely and efficiently under significant aerodynamic forces during outdoor missions. By integrating Extended Kalman Filtering with autoregressive feedforward neural networks, his system estimates critical flight parameters using only inertial measurement units (IMUs) and GPS, eliminating the need for traditional, often fragile, pitot-static probes. This work, published in 2018, has garnered 2 citations and lays a foundation for more robust, low-cost flight control in challenging environments. Jourdan’s approach directly addresses the practical challenges of high-speed aerial robotics, offering a path toward more resilient and autonomous fixed-wing platforms for applications ranging from surveillance to delivery.
Research Focus
Key Achievements
Top Papers
- 1