A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and...

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Vydáno v:IEEE sensors journal Ročník 24; číslo 5; s. 6539 - 6547
Hlavní autoři: Wei, Yuheng, Wei, Xinguo, Liu, Hao, Li, Jian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Angular distance matching
Data structures
graph representation algorithm
Graph theory
Graphical models
Identification
Identification methods
Matching
Night sky
Pattern matching
Position errors
Position measurement
Robustness
Satellite tracking
Satellites
Sensors
star identification
star sensor
Stars
Symbols
ISSN:1530-437X, 1558-1748
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Shrnutí:The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and their neighboring stars. However, these methods often fail when insufficient detectable stars or bright noncooperative space objects are in the field of view (FOV). Seldom studies use angular distance matching among all stars for direct identification as it is complex to calculate and store. To solve the problem, we proposed a graph algorithm based on angular distance matching score transfer, which utilizes a graph data structure, where nodes store angular distance matching scores between stars and transfer scores through graph edge weights, overcoming the limitations of incomplete utilization of angular distance matching results. We also established a math model for edge weights based on the probabilities of star pair occurrences. Simulation tests and night sky image experiments demonstrate the robustness of this algorithm against position errors, brightness errors, and fake stars. By equally using information from all sensor stars, even in cases with multiple missing stars, numerous fake stars, and extremely bright interfering stars, the identification rate remains consistently above 99.53%. This algorithm represents a highly robust star identification method and lays the foundation for future research in intelligent sensing of space environmental objects using star sensors.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3350089