星敏感器抗杂光的复合型背景估计星图处理算法

Composite Background Estimation Star Map Processing Algorithm for Star Sensor Resistant to Stray Light

摘要:

星敏感器在太空作业时,易受到太阳光、月光和地气光等杂光的干扰,导致拍摄的星图灰度整体升高,背景均匀性较差,难以准确提取到星点坐标。针对上述问题并结合现有算法提出了一种复合型背景估计的杂光干扰下星图处理算法。首先,星点具有经点扩散成像后尺寸为直径3×3至7×7的特征,设计相应的背景估计模板,最后为提高算法鲁棒性,提高局部信息的利用率,再设计一个像素估计模板,两个估计模板同时计算后处理数据实现阈值分割；最后进行星点质心计算。通过试验证明该方法可以较好地抵抗杂光干扰,提高杂光背景下星点提取的精度。

Abstract:

When the star sensor is operating in space, it is easy to be interfered by stray light such as sunlight, moonlight and earth-air light. As a result, the overall gray level of the star map taken is increased, the background uniformity is poor, and it is difficult to accurately extract the star coordinates. Aiming at the above problems and combining the existing algorithms, a composite background estimation star map processing algorithm under stray light interference is proposed. First of all, star points had the characteristics of dimensions ranging from 3×3 to 7×7 in diameter after point diffusion imaging, so the corresponding background estimation template was designed. Then, in order to improve the robustness of the algorithm and the utilization rate of local information, a pixel estimation template was designed. The two estimation templates calculate the post-processing data at the same time to achieve threshold segmentation, and then the centroid of star points was calculated. This method can better resist stray light interference and improve the accuracy of star extraction under stray light background.