Modeling and Filtering Colored Noise of a Star Tracker

This paper presents an attitude error model of a star tracker, which is induced from the optical system errors, and proposes an attitude Kalman filter considering the star tracker errors. Though it can be calibrated before and after launches, it is impossible to obtain error-free star tracker parameters in practice, which generates non-white noise errors in the star tracker outputs. Moreover, star tracker error models are usually a business secret for the manufacturers, so it is hard to estimate them online on the spacecraft bus. We model the attitude bias caused by the error of the optical parameters as colored noise using the camera model parameters and their covariance. A recursive form of the colored noise is derived based on a vector autoregressive model, and a colored noise Kalman filter is proposed to estimate the attitude error along with the spacecraft attitude and gyro bias. The proposed method only needs three additional states to be estimated and does not contain sensitive information for a star tracker manufacturer, which can ease the burden of its applications. The simulations illustrate the stability and reliability of the proposed algorithm.

Automated summary

This paper presents an attitude error model of a star tracker induced by optical system errors, and proposes an attitude Kalman filter considering the star tracker errors. It is challenging to estimate star tracker error models online due to the non-availability of error-free parameters and their confidentiality. The proposed method uses a colored noise Kalman filter to estimate attitude error, spacecraft attitude, and gyro bias, requiring only three additional states and not disclosing sensitive information of the star tracker manufacturer.