先验信息辅助的运动模糊星图星点提取方法

星敏感器处于高动态环境时所拍摄的星图会出现运动模糊现象,这将会严重影响星敏感器的星点提取精度.针对上述问题,提出了一种基于先验信息直接对高动态环境下的运动模糊星图进行星点提取的方法.首先,利用惯性导航系统和星敏感器的姿态先验信息预测当前运动模糊星图上的星点粗略坐标,将其作为“种子”点进行局部区域生长而获得运动模糊星图上的星像范围；然后,基于惯性导航系统的角速度输出估算星像运动轨迹长度并利用质心法计算星点坐标,根据星像运动轨迹长度与质心法提取误差之间的关系建立运动模糊星图星点提取误差修正模型；最后,基于上述模型对星点坐标进行修正,得到精度较高的运动模糊星图星点坐标. 仿真结果表明,相比于先对运动模糊星图进行复原再提取星点坐标的方法,本方法不仅能保证高动态环境下星敏感器的星点提取精度和较好的抗噪性能,同时还能大大减少星敏感器对运动模糊星图的处理时间,这有助于保证星敏感器的数据更新率不受高动态环境太大影响.

Star spot extraction method for motion blurred star image assisted by prior information

In highly dynamic environments, there can be motion blur on the star images taken by star sensors, which will seriously affect the star spot extraction accuracy. Regarding to such issues, a method is proposed in this paper to directly extract star spots form motion blurred star images assisted by prior information. First, the prior attitude information of inertial navigation systems and star sensors was utilized to predict the coarse star spot coordinates on the motion blurred star image, which were regarded as seed points for local region growth to obtain the range of the star spots. Then, the star spot motion trajectory was estimated based on the angular velocity output of the inertial navigation system, and star spot coordinates were calculated by the centroid method. Finally, errors of the star spot coordinates were corrected according to the relationship between the star spot motion trajectory and the centroid extraction error. Simulation results show that compared with the method that extracts star spots after restoring the motion blurred star image, the proposed method can ensure high accuracy of star spot extraction and good anti-noise performance under highly dynamic environments. Besides, the proposed method can greatly reduce the processing time for motion blurred star images, which is helpful to ensure that the data update rate of star sensors is not greatly affected by highly dynamic environments.