提高星敏感器星点定位精度是高精度星敏感器关键技术之一。常规质心法和高斯拟合法定位最小均方误差在0.1 pixel左右，难以满足未来高精度定位需求。在自适应光学领域，为研究Shack-Hartmann传感器波前定位而提出的迭代加权质心算法(IWCOG)，其定位误差优于前两种算法，但目前该算法的定位机理及多星定位误差特性未知。从Meanshift 理论角度推出该算法定位机制，证明算法的收敛性；每个仿真采用蒙特卡罗方法随机生成10000个星点样本，通过分析星点参数、信噪比与定位误差的关系研究IWCOG 算法在星敏感器多星定位的特点。模拟结果显示，在信噪比R=40 下，IWCOG 法星点提取误差低于0.02 pixel的样本占44%，低于0.04 pixel的占72%，远高于质心法的0.8%和1.9%，基本满足甚高精度星敏感器的定位要求。

Improving the positioning accuracy of star spots is still one of crucial technologies in high precision star trackers. The minimum meansquared error of the conventional center of gravity (COG) and Gaussian curve fitting (GCF) algorithm for positioning is about 0.1 pixel, hardly to meet the future demand for highprecision positioning. In the field of adaptive optics, an iteratively weighted center of gravity (IWCOG) is proposed for wave- front localization of Shack- Hartmann sensor with superior performance to the first two algorithms in locating accuracy. However, the positioning mechanism of IWCOG and the performance on the multi-star positioning accuracy are currently unknown. On the one hand, this paper introduces the positioning mechanism of the algorithm from a theoretical perspective of Meanshift and proves the convergence of the IWCOG algorithm. On the other hand, 10000 randomly star spots are generated with Monte Carlo method in every single simulation to study the influence of IWCOG algorithm on multi- star positioning accuracy in star tracker by studying relationship between the star point parameters, signal to noise ratio and positioning errors. The result shows that there are 44% samples with positioning error lower than 0.02 pixel and 72% with error lower than 0.04 pixel using IWCOG algorithm under signal to noise ratio R equal to 40, much better than the COG with only 0.8% and 1.9% , basically meeting the positioning requirements of super- high accuracy star tracker.