高动态条件下星点像斑建模与补偿

高动态条件下星点提取有两个难点：一是暗弱星点目标湮没在噪声中不易识别，质心定位精度较差；二是星点像斑可能断裂，无法用常用的连通域算法提取。针对上述难点，本文建立了高动态条件下星点像斑模型，提出了基于该模型的像斑补偿与质心定位提取算法。该方法分为四步：第一，利用卡尔曼滤波实现星点像斑静态模板的实时迭代；第二，基于静态模型与星点运动模糊模型建立星点像斑动态模型；第三，以动态模型作为模板在恒星跟踪窗口内进行相关性匹配以确定星点像斑位置；第四，基于动态模型补偿星点像斑，并计算质心位置。实验结果表明，该方法能有效解决高动态条件下暗弱星点提取与断裂残损星点修复问题。相比传统算法，姿态精度误差均值减少了40.9%，最大误差减少了81.2%；星点提取率达到100%，提高174.5%，提取星数相比阈值分割与连通域法提高了173%。

Modeling and Compensation of Star Spot in High Dynamic Condition

There are two difficulties in star spot extraction in high dynamic conditions. Firstly, dim and small stars are difficult to identify in noise, and the accuracy of star centroid is poor. Secondly, the star spots may be damaged, which cannot be extracted by common domain algorithm. In view of the above difficulties, the star spot model under high dynamic conditions was established. The star spot compensation and centroid location algorithm was proposed based on the model. The algorithm includes four steps. Firstly, Kalman filter was used to iterate the static model of star spot in real time. Secondly, the dynamic model of the star spot was established based on the static model and the motion blur model. Thirdly, the dynamic model, as a template for correlation matching in the tracking window, was used for the position determination of star spot. Fourthly, the star spot was compensated based on the dynamic model, and the star centroid was calculated. Simulation results showed that the algorithm can effectively solve the problem of dim mall star spot extraction and damaged star spot restoration in high dynamic conditions. Compared with the traditional algorithm, the mean error of attitude accuracy is reduced by 40.9%, the maximum error is reduced by 81.2%, the star extraction rate is 100%, the star extraction rate is increased by 174.5%, and the threshold segmentation is improved by 173% compared with the combined domain method.