Abstract
Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed star map is often difficult to use, because they might contain errors or be lost in some worst cases. To solve this problem, we proposed a multi-view double-triangle algorithm for star identification in this paper. This algorithm constructs double-triangle features of stars with the angle and distance information of star points. Moreover, to reduce the influence of noise interference on the identification accuracy of the model, we built multi-view double-triangle features for the observed star map to improve the robustness of the algorithm. Synthetic and real experiments show that our algorithm has a high identification accuracy of more than 98.4% in face of “false star” noises and “missing star” noises, and our algorithm is not affected by the focal length and the shooting angle of the star sensor. Moreover, the results also show that our algorithm has good robustness, short identification time and reduced storage costs, which could be beneficial in practice.
Highlights
Autonomous celestial navigation, known as astronavigation, is an independent, unconditional and global autonomous navigation system [1], which is widely used in many countries worldwide
We compared our Multi-View Double-Triangle Algorithm for Star Identification (MVDT-SI) algorithm with following algorithm settings: Single-view single-triangle algorithm for star identification (SVST-SI): This algorithm uses a single triangle in the first view of the reference star and the auxiliary star to construct the feature of the observed star map
In the case of false stars or missing stars added to the observed star map, geometric voting algorithm (GMV) and search tree with optimized database (STOD) will fail drastically
Summary
Autonomous celestial navigation, known as astronavigation, is an independent, unconditional and global autonomous navigation system [1], which is widely used in many countries worldwide. The star sensor/tracker is the main part of the celestial navigation system [2], which is an optical measurement device that measures the attitude information of stars using photocells or a camera. The main function of the star sensor includes: star centroid estimation, star identification and attitude calculation. There are lots of star centroid estimation methods [3,4,5], which are applied to calculate the centroid of stars. These methods play an important role in improving the precision of attitude determination for star sensors. The most challenging part is the star identification, which creates the correspondence between observed and cataloged stars [6]
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