A comparison of statistical methods for fitting telescope pointing models

Telescope mount models use a mathematical model to introduce commanded position adjustments to compensate for predictable pointing errors. The parameters of the model are estimated from observed pointing deviations on a set of calibration stars. These calibration measurements generally contain random noise and other features that limit the precision of the parameter estimates and ultimately degrade pointing. This paper compares the ability of various statistical solution methods to improve the precision of the parameter estimates and improve pointing.