基于同心圆分割的大视场星图识别算法

doi:10.11772/j.issn.1001-9081.2013.07.1984

计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 1984-1987.DOI: 10.11772/j.issn.1001-9081.2013.07.1984

基于同心圆分割的大视场星图识别算法

刘恒¹,²,郑烇¹,²,秦龙¹,²,赵天昊¹,²,王嵩¹,²

1. 网络传播系统与控制安徽省重点实验室(中国科学技术大学)，合肥230027
2. 中国科学技术大学信息科学技术学院，合肥230027

收稿日期:2013-01-29 修回日期:2013-03-11 出版日期:2013-07-01 发布日期:2013-07-06
通讯作者: 郑烇
作者简介:刘恒(1986-)，男，广西玉林人，硕士研究生，主要研究方向：网络多媒体、模式识别；郑烇(1970-)，男，安徽合肥人，副教授，博士，主要研究方向：网络多媒体、模式识别、视频语义检索、媒体内容分发；秦龙(1988-)，男，湖北仙桃人，硕士研究生，主要研究方向：网络多媒体；赵天昊(1987-)，男，黑龙江齐齐哈尔人，硕士研究生，主要研究方向：网络多媒体；王嵩(1975-)，男，安徽六安人，讲师，博士，主要研究向：计算机网络、媒体内容分发。
基金资助:
超精密控制与系统联合实验室科研项目;国家发改委CNGI课题(CNGI-09-03-14)

Star pattern recognition algorithm of large field of view based on concentric circles segmentation

LIU Heng¹,²,ZHENG Quan¹,²,QIN Long¹,²,ZHAO Tianhao¹,²,WANG Song¹,²

1. Key Laboratory of Network Communication System and Control of Anhui (University of Science and Technology of China), Hefei Anhui 230027, China
2. School of Information Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China

Received:2013-01-29 Revised:2013-03-11 Online:2013-07-06 Published:2013-07-01
Contact: ZHENG Quan
Supported by:
;CNGI project of National Development and Reform Commission

摘要/Abstract

摘要： 针对星敏系统常见的三角形识别算法数据冗余量大、识别速度特别是初始识别速度低的问题，提出一种基于同心圆分割的大视场(FOV)星图识别算法。在分析星图信息以获得其主星的基础上，围绕主星以一定的半径画8个同心圆，再根据各星的坐标统计每个圆环内的星数量，从而得出主星的伴星分布向量。以同样方法基于基本星表构建相对应的导航星特征库，然后利用伴星分布向量与特征库进行模式匹配，从而得出星图识别结果。对特征库中的数据，根据各向量的第一维元素大小进行排序，以加快算法的识别过程。仿真实验结果表明，该算法所需的导航星特征库存储空间小，具有良好的实时性、抗噪性与较高的识别率，能够以95.3μs的识别时间达到88.9%以上的正确率，可与其他识别算法相结合，执行于不同的阶段，实现更高效、准确的天文导航。

关键词: 同心圆, 大视场, 星图识别, 伴星分布向量, 导航星特征库

Abstract: Since the triangle identification algorithm commonly utilized in star sensitive system is of high data redundancy and low recognition speed, especially initial recognition speed, a concentric circles-based star pattern recognition algorithm of large Field Of View (FOV) was proposed. After analyzing the information of star map to acquire its main star, draw eight concentric circles around the main star at some certain radiuses, then figure out the number of stars in each annulus based on the coordinates to obtain the distributional vector of companion stars. Construct the navigation star feature database from the base database with the utilization of the same method, so as to process the pattern matching with the distributional vector to acquire star pattern recognition result. The vectors in the database will be sorted by the first dimensional element in order to accelerate the process of recognition. The simulation results show that this algorithm needs much less storage space of navigation star feature database, and possesses good real-time and noise resistance, and high recognition rate. It takes 95.3μs recognition time to achieve more than 88.9% accuracy, and it also can be integrated with other recognition algorithms and performance in different stages to realize more efficient and accurate celestial navigation.

Key words: concentric circle, large Field Of View (FOV), star pattern recognition, distributional vector of companion stars, navigation star feature database

中图分类号:

TP391.413

刘恒郑烇秦龙赵天昊王嵩. 基于同心圆分割的大视场星图识别算法[J]. 计算机应用, 2013, 33(07): 1984-1987.

LIU Heng ZHENG Quan QIN Long ZHAO Tianhao WANG Song. Star pattern recognition algorithm of large field of view based on concentric circles segmentation[J]. Journal of Computer Applications, 2013, 33(07): 1984-1987.

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基于同心圆分割的大视场星图识别算法

Star pattern recognition algorithm of large field of view based on concentric circles segmentation

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