Hyperspectral Lightcurve Inversion for Attitude Determination
This addresses attitude determination for spacecraft, but it is incremental as it builds on existing methods without major breakthroughs.
The paper tackled the problem of inferring spacecraft attitude and rotation from spectral lightcurves without prior information, using numerical optimization and neural network methods, and demonstrated results on synthetic data.
Spectral lightcurves consisting of time series single-pixel spectral measurements of spacecraft are used to infer the spacecraft's attitude and rotation. Two methods are used. One based on numerical optimisation of a regularised least squares cost function, and another based on machine learning with a neural network model. The aim is to work with minimal information, thus no prior is available on the attitude nor on the inertia tensor. The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data. Results are shown based on synthetic data.