Paper
12 March 2020 Modeling method and discriminant criterion for single-particle image
Haiyong Wang, Guoyuan Tian, Hongjun Zhong, Long Wang
Author Affiliations +
Proceedings Volume 11436, 2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications; 114360X (2020) https://doi.org/10.1117/12.2550361
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China
Abstract
Single-particle image spot on star map of star sensor can be mistaken for real star image and incur fault star identification, how to treat space single-particle properly in a star sensor is an emerging serious problem. Analysis and modeling are conducted for two types of single-particle image which are of spot shape and stripe shape. By observing two extreme cases of center location (pixel center or corner vertex) of a Gaussian star image and after examining of their Gaussian gray distribution characteristics, the discriminant criterion of single-particle image spot is established with a form of gray interval which is different with the pixel number of connected domains. Another discriminant criterion is also set up according to morphology, which is a limit value calculated from an equation expressing the slenderness of a star image. Simulating test results indicate that the involvement of the discriminant criterion can effectively identify single-particle images from real star ones, which helps to promote the reliability of attitude determination of star sensor.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haiyong Wang, Guoyuan Tian, Hongjun Zhong, and Long Wang "Modeling method and discriminant criterion for single-particle image", Proc. SPIE 11436, 2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, 114360X (12 March 2020); https://doi.org/10.1117/12.2550361
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KEYWORDS
Stars

Star sensors

Image sensors

Digital imaging

Detection and tracking algorithms

Optical spheres

Shape analysis

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