当星敏感器在动态条件下工作时, 在成像的过程中星点会因能量的分散而出现运动模糊现象, 导致图像的信噪比降低, 星点的模糊区域很难被提取, 从而降低了星点质心的定位精度, 严重影响星敏感器的姿态测量精度。为此, 文中提出了一种基于区域滤波的模糊星图复原方法, 在有效提高图像信噪比的同时可提高星点质心的定位精度。首先, 根据星敏感器的工作特性, 建立了不同工作条件下星点质心的运动模型。然后, 根据运动模型确定星点质心的运动轨迹, 进而提取星点的模糊区域, 再利用图像处理算法对模糊区域内外的图像分别进行预处理。最后, 利用图像复原算法对模糊星图进行复原。实验结果表明: 在2(°)/s的动态条件下, 区域滤波算法能够有效提高模糊星图的信噪比, 并且复原图像中星点质心的定位误差不超过01 pixel, 可以满足星敏感器对高质心定位精度的要求。

Under the dynamic working conditions for a star sensor, motion blur will appear in a star because of its energy dispersion in the process of imaging, which leads to a decrease in the signal to noise ratio(SNR) and makes the blurred region difficult to extract. Meanwhile, this causes a degradation in star centroid positon accuracy and attitude accuracy in the star sensor. Therefore, a restoration method for blurred star images based on region filters is presented in this paper, which simultaneously concentrates on the improvement of SNR and star centroid accuracy. Firstly, the kinematic models of a star centroid under different conditions are set up based on the characteristics of star sensors. Secondly, the motion trail of star centroid is determined based on the kinematic model, allowing the star blurred region to be extracted. The images inside and outside the star blurred region are then preprocessed by image processing algorithm respectively. Finally, the blurred star image is restored by an image restoration algorithm. The experiment results indicate that under the dynamic condition of 2°/s, the region filter algorithm can effectively improve the SNR of a blurred star image. In restored images, the error of star centroid is less than 01 pixels, which can satisfy the requirements for star sensor of high centroid accuracy.