Offline calibration method of low frequency error of star sensor and gyroscope drift

High-accuracy post attitude data is critical to the improvement of image quality of remote sensing platforms. During offline processing, errors of attitude sensors can be efficiently calibrated to achieve higher precision of attitude determination. However, coupling influence of low frequency error (LFE) and gyroscope drift can cause the decrease of calibration precision. In order to solve the problem, a mathematical model of the influence is derived in this paper. Meanwhile, a two-step bidirectional smoothing algorithm is proposed to calibrated separately gyroscope drift and LFE. Gyroscope drift and LFE can be perfectly separated with the proposed method. In order to solve the problems of slow convergence of LFE parameters and the difficulty of tuning noise parameters a maximum-likelihood-estimation (MLE) based bidirectional adaptive filtering algorithm is developed, which can improve both convergence speed and precision dramatically. Under the simulation condition in this paper, the accuracy of offline attitude determination reaches 0.8″(3σ) and the convergence time of LFE parameters is not more than 4 orbital periods.