Multidimensional optimization-improved grid star map recognition algorithm
In high-precision celestial navigation, star map recognition algorithms are crucial. We identified limitations in the classical grid star map recognition algorithm (CGSMRA) concerning star sorting method, selection strategy, scoring criterion, and screening mechanisms. To address these, we developed a multidimensional optimization-improved grid star map recognition algorithm (MOIGSMRA). We evaluated MOIGSMRA through five experiments: template matching efficiency, companion star recognition, recognition accuracy, attitude determination accuracy, and overall performance. Compared to CGSMRA, MOIGSMRA demonstrated superior results. This study offers a method to optimize attitude determination algorithms for star sensors and provides a theoretical and experimental foundation for improving star recognition accuracy.
Authors
- Jian Zhang (ORCID: https://orcid.org/0000-0003-3831-485X)
- Zhikun Yun
- Jingrui Sun (ORCID: https://orcid.org/0000-0002-7101-9169)
- Junjie Yang (ORCID: https://orcid.org/0000-0002-7534-9077)
- Dongpeng Yang (ORCID: https://orcid.org/0000-0002-6950-5985)
- Guoyu Zhang (ORCID: https://orcid.org/0000-0001-6085-0910)
- Songzhou Yang (ORCID: https://orcid.org/0009-0002-5597-047X)
- Yu Zhang (ORCID: https://orcid.org/0009-0006-0165-9124)
- Xiangkai Meng (ORCID: https://orcid.org/0000-0001-6659-6749)
- Taiyang Ren
- Bin Zhao (ORCID: https://orcid.org/0000-0001-9636-8675)
Institutions
- Changchun University of Science and Technology (CN)
Publication Details
- Journal
- Optics Express
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1364/oe.538070
- Citations
- 1
- Primary Topic
- Inertial Sensor and Navigation
- Type
- article
- Field-Weighted Citation Impact
- 1.30
Funders
- National Natural Science Foundation of China
- Higher Education Discipline Innovation Project
- Jilin Scientific and Technological Development Program