On-Orbit Calibration

D. T. Griffith, P. Singla, J. L. Junkins, "Autonomous On-Orbit Calibration Approaches for Star Tracker Cameras," AAS/AIAA Spaceflight Mechanics Meeting, No. 02-102, AAS, San Antonio, TX, Jan 27-30, 2002.

In addition to a batch calibration, we need on-orbit calibration because of the environmental changes over the life of the camera film. This paper describes the star tracker camera and proposes a way to estimate the distortion map of the star locations on-orbit (almost same word as the 'online' in machine learning), assuming the offsets and the focal length are given.
Basically they use the least square method with a couple basis function sets such as polynomials, sinusoidal functions or radial basis functions. By the health monitoring process, it updates the map only when the error is greater than a threshold.

It also includes a review of the least square method and the recursive least square method, which, by the way, looks like Kalman filter. The piecewise approximation is not an interesting part because usually the map would be smooth enough so that a few basis function might be good enough. And, this piecewise approximation needs a lot of basis functions which is computationally so expensive.


Nice paper for novices like me in the on-orbit calibration problem.

- H. Choi

Finding offsets and the focal length of a camera

M. A. Samaan, "Toward Faster and More Accurate Star Sensors Using Recursive Centroiding and Star Identification," Ph.D. Thesis, Texas A&M University, August 2003.

Chapter 3 is titled 'Ground Calibration for the Bore-Sight Offsets and the Focal Length.'

He uses the least square minimization (LSM) method recursively to find optimum offsets and the focal length which minimizes the error between inner product of reference stars and the estimated inner product of measured stars.

- H. Choi