  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.
