Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 5; p. 1661
• Main Authors: Shen, Chao, Ma, Caiwen, Gao, Wei, Wang, Yuanbo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.03.2024; MDPI
• Subjects: Algorithms; Artificial intelligence; Calibration; Cameras; Cost control; hybrid grid pattern; Identification; multi-calibration star verification; Neural networks; Optimization algorithms; Pattern recognition; Random variables; reference star map verification; Sensors; star identification; Star maps; hybrid grid pattern; reference star map verification; multi-calibration star verification; star identification
• DOI: 10.3390/s24051661
• ISSN: 1424-8220

In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.