光学 精密工程, 2017, 25 (1): 208, 网络出版: 2017-03-10
基于最大内角的三角形星图识别算法
Star identification algorithm based on the maximum interior angle in triangle
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13.1 ms。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
As the traditional triangle star identification algorithm is insufficient, this paper proposed a fast all-sky autonomous triangle algorithm with star magnitude-independent. By structuring the maximum interior angle and two sides as a matching feature triangle, the algorithm established celestial navigation feature library which was constructed to be a hash function according to the maximum interior angle and stored into sub-blocks. ‘Edge-angle-edge’ matching mode was adopted in the process of star identification. First, adopt the hash search to achieve rapid positioning of sub-blocks in terms of observations of the maximum interior angle, and then quick matching of star argument was conducted on both sides of the observed interior angle, which would further to narrow matching scope of the argument and improve identification speed. Experiments indicate that identification rate of the algorithm can exceed 98.08% when star point noise is lower than 2 pixels and average identification time is 13.1 ms when observed stars number equals to 10 and the sum of sub-blocks in feature library is 1 024. Compared with current triangle identification algorithms, this algorithm has obvious advantages in identification speed, identification rate and the ability of resisting star magnitude noise.
张同双, 郭敬明, 柏杨, 刘冰, 周海渊, 王二建, 张世学. 基于最大内角的三角形星图识别算法[J]. 光学 精密工程, 2017, 25(1): 208. ZHANG Tong-shuang, GUO Jing-ming, BAI Yang, LIU Bin, ZHOU Hai-yuan, WANG Er-jian, ZHANG Shi-xue. Star identification algorithm based on the maximum interior angle in triangle[J]. Optics and Precision Engineering, 2017, 25(1): 208.