Nathan Stacey
Papers
1
Total Citations
14
H-Index
1
About
Dr. Nathan Stacey is a leading researcher in spacecraft autonomy and vision-based navigation, with a focus on enabling robust operations for uncooperative space objects. His key research areas include pose estimation, sensor fusion, and adaptive filtering for space rendezvous and proximity operations. Dr. Stacey’s most notable contribution is the development of an adaptive Convolutional Neural Network (CNN)-based Unscented Kalman Filter that leverages neural network uncertainty to dramatically improve spacecraft pose estimation accuracy. This work, published in 2023 and already garnering 14 citations, was rigorously validated at Stanford’s robotic Testbed for Rendezvous and Optical Navigation on the Satellite Hardware-In-the-loop Rendezvous facility, demonstrating real-world applicability. By integrating deep learning uncertainty quantification with classical filtering, his approach addresses critical challenges in estimating the position and orientation of uncooperative spacecraft—a key capability for debris removal, on-orbit servicing, and future autonomous missions. Dr. Stacey’s research bridges the gap between modern machine learning and traditional aerospace estimation, offering practical solutions that are shaping the next generation of autonomous space systems.
Research Focus
Key Achievements
Top Papers
- 1