The method of Principal Components Analysis (PCA) is widely used in statistical data analysis for engineering and the sciences. It is an effective tool for reducing the dimensionality of datasets while retaining majority of the data information. This paper explores the method of using PCA for spacecraft pose estimation for the purpose of proximity operations, and adapts a novel kernel based PCA method called Euler-PCA to denoise spacecraft imagery using a single optical or thermal camera.

Additional Metadata
Persistent URL dx.doi.org/10.2514/6.2017-1034
Conference AIAA Guidance, Navigation, and Control Conference, 2017
Citation
Shi, J.-F. (Jian-Feng), Ulrich, S, & Ruel, S. (Stephane). (2017). Spacecraft pose estimation using principal component analysis and a monocular camera. In AIAA Guidance, Navigation, and Control Conference, 2017. doi:10.2514/6.2017-1034