基于最优背景估计的星敏感器抗杂散光星点提取方法

研究了杂散光干扰下星敏感器星点提取问题。在杂散光干扰下，星点提取的效果会变差，这会使解算的姿态不准确和不可靠。为了抵抗杂散光干扰，提出了一种基于最优背景估计的抗杂散光星点提取方法。首先，分析了杂散光在星敏感器中的成像特点，构建了杂散光的具有闭合解的曲面模型；然后，设计了基于最优曲面的杂散光背景估计方法和星点提取方法；最后，采用仿真的杂散光图像和星敏感器采集的杂散光图像验证了算法的性能。实验结果表明:该方法得到的星点检测率、误检率和质心定位精度均优于现有基于背景和形态学的方法，该方法对杂散光有较好的抑制能力。

Star spot extraction based on optimal background estimation for star sensor anti stray light

In this article, the problem of extracting star spots for star sensor in stray light is considered. Under this setting, the effect of extracting star spots becomes worse, which affects the accuracy and reliability of the estimated attitude. An extraction method based on optimal surface fitting is proposed for this task. First, the imaging characteristics of stray light in star sensor are analyzed, and the surface model with a closed-form solution is built. Then, the method of estimating optimal stray light background and extracting star spots is proposed. The performance of the proposed method is verified by the simulated star images and star sensor images. Experimental results show that the detection rate, false detection rate and centroid accuracy obtained by the proposed method are better than that obtained by the methods based on threshold and morphological, which shows that the proposed method can resist the disturbance of stray light.