ABOUT THIS POSITION

Help shape the future of secure navigation! Modern society, and especially the military, relies heavily on Position, Navigation and Timing (PNT) data from Global Navigation Satellite Services (GNSS) like GPS. But GNSS signals are fragile and vulnerable to interference. TNO’s Autonomous Systems & Robotics group is developing TOPS, a cutting-edge software suite that fuses data from multiple sensors to ensure reliable navigation, even in challenging environments.

What is the challenge?
TOPS uses advanced filtering techniques to combine noisy sensor data in real-time. But when orientation is involved, things get tricky: the space of orientations is non-linear, requiring specialized mathematical tools like Lie group filtering.
As an MSc intern, you’ll explore and contribute to the mathematical backbone of robust navigation systems.

Your tasks will include:

• Explore the geometry of orientation: Understand the role of Lie groups in modeling rotational dynamics.
• Study filtering and observer theory: Learn how classical filtering methods (e.g., Kalman filters) are adapted to non-linear and curved spaces.
• Implement and compare filtering techniques: Benchmark standard filters (e.g., UKF) against Lie group-based filters using Python and real-world datasets.
• Contribute to TOPS development: Help improve the accuracy and robustness of TNO’s sensor fusion framework with your scientific insights.