Robust polygon recognition method with similarity invariants applied to star identification

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• 作者：E. Antonio Herná ; ndez ; ^{helias@cicese.edu.mx} ; Miguel A. Alonso ; Edgar Chá ; vez ; David H. Covarrubias ; Roberto Conte
• 关键词：Star identification ; Star sensor ; Star tracker ; Similarity transformation ; Polygon recognition ; Invariant
• 刊名：Advances in Space Research
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：59
• 期：4
• 页码：1095-1111
• 全文大小：2417 K
• 卷排序：59

文摘

In the star identification process the goal is to recognize a star by using the celestial bodies in its vicinity as context. An additional requirement is to avoid having to perform an exhaustive scan of the star database. In this paper we present a novel approach to star identification using similarity invariants. More specifically, the proposed algorithm defines a polygon for each star, using the neighboring celestial bodies in the field of view as vertices. The mapping is insensitive to similarity transformation; that is, the image of the polygon under the transformation is not affected by rotation, scaling or translations. Each polygon is associated with an essentially unique   complex number. We perform an exhaustive experimental validation of the proposed algorithm using synthetic data generated from the star catalog with uniformly-distributed positional noise introduced to each star. The star identification method that we present is proven to be robust, achieving a recognition rate of 99.68% when noise levels of up to ±±424 μμ radians are introduced to the location of the stars. In our tests the proposed algorithm proves that if a polygon match is found, it always corresponds to the star under analysis; no mismatches are found. In its present form our method cannot identify polygons in cases where there exist missing or false stars in the analyzed images, in those situations it only indicates that no match was found.