摘要
为了快速高效地对星图进行识别,准确地完成天文导航任务,提出了一种基于剖分特征集星识别方法。首先使用星表数据建立数据库,三角剖分该数据库建立特征星库,再获取待识别星图剖分特征与已建立的特征星库相比较实现星识别。在改进海明相似度与Euclid相似度等相似方法基础上,提出了一种新的剖分特征集星识别法,使用该方法可以快速地找到一个很小的可能星集合,重复该方法再获得相邻星的可能星集合,两个星集合中赤经与赤纬最相近的就是识别星。实验显示使用剖分特征集星识别法,准确率可以达到97%以上,能够准确地完成星图识别任务。
A star pattern recognition method based on subdivisiom feature set was proposed in order to quickly and efficiently recognize star pattern and accurately complete celestial navigation task. Firstly, a database was built by star data, on which feature star database was established by triangulation. Then subdivision feature of star pattern could be compared, which will be recognized, with feature star database and implement star pattern recognition. By improving some similar methods like Hamming similarity and Euclid similarity, a new star pattern recognition method was put forward based on triangulation feature set, by which a very small possible star set could be found. This process can be repeated to obtain adjacent possible star set. In these two star sets, the nearest star between right ascension and declination was the one recognized. Experiments show that accuracy rate can reach more than 97% by using this method, and the star pattern recognition task can be completed accurately.
出处
《红外与激光工程》
EI
CSCD
北大核心
2015年第11期3330-3335,共6页
Infrared and Laser Engineering
基金
黑龙江省自然科学基金(F201424)
关键词
剖分特征集
相似度
星图识别
德劳内三角剖分
triangulation feature set
similarity degree
star pattern recognition
Delaunay triangulation
