Motion parameters estimation algorithm of star sensor based on Zernike moments under dynamic conditions

Abstract The dynamic performance of star sensors is primarily affected by the smearing effect of star spots. To enhance the dynamic performance, it is necessary to perform blur kernel estimation and restoration of star images. However, the accuracy of motion parameters estimation of existing algorithms is not high enough, which limits the restoration effect. An efficient motion-blurred star image’s motion parameters estimation algorithm based on Zernike moments is presented in this study to upgrade the accuracy of centroid extraction and attitude estimation under dynamic conditions. The proposed algorithm improves the dynamic performance of star sensors through two aspects. First, a unitized roof model is established for the dynamic star spots. Second, the Zernike moments of the unitized roof model are derived, and the subpixel edge detection method is used to estimate the angle and length of the star streak for obtaining the motion parameters of the star image. The effectiveness and excellent performance of the proposed algorithm are verified using simulations and ground experiments. Experiments reveal that this algorithm is robust to noise, increases the estimation accuracy of blur parameters, and thus improves the accuracy of star spot extraction. Meanwhile, the algorithm has less time consumption, and is suitable for boosting the dynamic performance of star sensors.