摘要
星敏感器是高精度的姿态测量部件,在各种航天、航空飞行器的姿态测量或控制系统中发挥着关键作用。作为星敏感器的核心技术,可靠、快速和高精度的星图识别算法一直是重要的研究课题。论文对星图识别算法进行研究。针对基于奇异值分解的星图识别算法可能出现的由于视轴不连续所造成的全天区覆盖率较低问题,提出了一种改进的基于奇异值分解的星图识别算法,详细阐述了各部分算法的设计思想。论文最后在JDK5.0开发环境中用JAVA语言实现了改进算法,并与传统的三角形算法的性能进行了比较详尽的对比。
Star tracker is most precise instrument of attitude measurement,playing a vital role in attitude measurement and control system of all kinds of Aerospace Flight Vehicles. Star pattern recognition algorithm as one of core technology of star tracker is so important that the reliable,fast and accurate star pattern recognition is widely investigated. The star pattern recognition algorithms are investigated. For the problem of low coverage throughout the sky caused by lack of bore sight direction,an improved singular value method for recognition algorithm is proposed respectively. The design thoughts of programs are elaborated clearly. Finally,this thesis develops JAVA language code of the improved algorithm in the JDK 5. 0Builder environment and makes an comparison with performance of the traditional Triangle algorithm in detail.
出处
《智能计算机与应用》
2014年第2期21-24,28,共5页
Intelligent Computer and Applications
基金
国家自然科学基金(41071262)
关键词
星敏感器
星图识别算法
导航星数据库
奇异值分解
Star Tracker
Star Recognition Algorithms
Database of Guide Star Pattern
Singular Value Decomposition
