基于最大内角的三角形星图识别算法

发布时间：2018-04-30 13:29

本文选题：星敏感器 + 星图识别　；参考：《光学精密工程》2017年01期

【摘要】：针对传统三角形星图识别算法的不足,本文提出了一种不依赖星等信息的全天球自主快速三角形识别算法。通过构建三角形最大内角及其两边作为匹配特征三角形,建立了全天球导航特征库,对生成的特征库按最大内角值构造散列函数,并分块存储。识别过程中,采用"边-角-边"原理进行匹配。首先,根据最大内角的观测值实现子块的快速定位,然后,在子块中对观测三角形的两边进行星角距快速匹配,缩小了角距匹配的范围,提高了识别速度。试验表明,星点位置噪声低于2个像元时,识别率优于98.08%;观测星数等于10颗,特征库分块总数为1 024时,平均识别时间为13.1ms。与现有三角形识别算法相比,该算法在识别速度、识别率及抗星等噪声能力等方面具有明显优势。
[Abstract]:In order to overcome the shortcomings of the traditional star map recognition algorithm, this paper proposes a fast and autonomous triangle recognition algorithm for the whole celestial sphere, which does not depend on star information. By constructing the maximum inner angle of the triangle and its two sides as matching characteristic triangles, the all-day spherical navigation feature library is established. The hash function of the generated feature library is constructed according to the maximum inner angle value, and stored in blocks. In the process of recognition, the principle of "edge-angle-edge" is used to match. Firstly, the fast location of the sub-block is realized according to the observation value of the maximum inner angle, and then the star-angle distance between the two sides of the observation triangle is quickly matched in the sub-block, which reduces the range of the angular distance matching and improves the recognition speed. The experimental results show that the recognition rate is better than 98.08 when the star position noise is less than 2 pixels, the number of observation stars is equal to 10, the total number of feature blocks is 1 024, and the average recognition time is 13.1 Ms. Compared with the existing triangle recognition algorithm, this algorithm has obvious advantages in recognition speed, recognition rate and the ability to resist star noise.
【作者单位】：中国卫星海上测控部;飞行器海上测量与控制联合实验室;中国科学院长春光学精密机械与物理研究所;吉林大学生物与农业工程学院;
【基金】：航天科学基金资助项目(No.2011-JS-000675)
【分类号】：V448.2;TP391.41

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