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Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Published online by Cambridge University Press:  13 June 2022

Chonghui Li Open the ORCID record for Chonghui Li [Opens in a new window] ,
Yuanxi Yang ,
Guorui Xiao ,
Zhanglei Chen ,
Shuai Tong  and
Zihao Liu
Chonghui Li
Affiliation:
State Key Laboratory of Geo-information Engineering, Xian, Shanxi, 710054, the People's Republic of China Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, the People's Republic of China
Yuanxi Yang
Affiliation:
State Key Laboratory of Geo-information Engineering, Xian, Shanxi, 710054, the People's Republic of China
Guorui Xiao*
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, the People's Republic of China
Zhanglei Chen
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, the People's Republic of China
Shuai Tong
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, the People's Republic of China
Zihao Liu
Affiliation:
Institute of Geospatial Information, Information Engineering University, Zhengzhou, Henan, 450001, the People's Republic of China
*
*Corresponding author. E-mail: [email protected]
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Abstract

The fish-eye star sensor with a field of view (FOV) of 180° is an important piece of equipment for attitude determination, which improves the visibility of stars significantly. However, it also brings the star identification (star-ID) difficulties because of imprecise calibrations. Thus, a fish-eye star-ID algorithm supported by the integration of the precise point positioning/inertial navigation system (PPP/INS) is proposed. At first, a reference star map is generated in combination with the distortion model of the fish-eye camera based on the position and attitude information from the PPP/INS. Then the star points are extracted in a specific neighbourhood of the reference star points. Subsequently, the extracted star points are individually tested and identified according to angular distance error. Finally, the real-time precise attitude is determined based on the star-ID results. Experimental results show that, 270–310 stars can be identified in a fish-eye star map with an average time of 0.03 s if the initial attitude error is smaller than 1.5° and an attitude determination accuracy better than 10″ can be achieved by support from PPP/INS.

Keywords

integrated navigation system (INS)attitude and heading reference system (AHRS)multi-sensor navigationimagingprecise point positioning (PPP)
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 4 , July 2022 , pp. 928 - 945
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Arani, M. S., Toloei, A. and Eghbaleh, Z. (2015). A geometric star identification algorithm based on triple triangle map, International Conference on Recent Advances in Space Technologies, IEEE, 81–85.CrossRefGoogle Scholar
Christian, J. A. and Crassidis, J. L. (2021). Star identification and attitude determination with projective cameras. IEEE Access, 9, 25768–25794.CrossRefGoogle Scholar
Duan, Y., Li, M., Niu, Z., Jing, P. and Chen, Z. (2018). A star pattern recognition algorithm for cameras with large FOV. Journal of Modern Optics, 65(1), 85–97.CrossRefGoogle Scholar
Hernández, E. A., Alonso, M. A., Chávez, E., Covarrubias, D. H. and Conte, R. (2016). Robust polygon recognition method with similarity invariants applied to star identification. Advances in Space Research, 59, 1095–1111.Google Scholar
Hughes, C., Denny, P., Jones, E. and Glavin, M. (2010). Accuracy of fish-eye lens models. Applied Optics, 49(17), 3338–3347.CrossRefGoogle ScholarPubMed
Jin, S. G. and Su, K. (2020). PPP models and performances from single- to quad-frequency BDS observations. Satelliate Navigation, 1(1), 16. doi: 10.1186/s43020-020-00014-yCrossRefGoogle Scholar
Juang, J. N., Kim, H. and Junkins, J. (2003). An efficient and robust singular value method for star pattern recognition and attitude determination. Advances in the Astronautical Sciences, 115(1), 211–220.Google Scholar
Kannala, J. and Brandt, S. (2006). A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8), 1335–1340.CrossRefGoogle ScholarPubMed
Kaplan, G. H., Bangert, J. and Bartlett, J. L. (2009). Naval observatory vector astrometry software (NOVAS) version 3.0. Bulletin of the American Astronomical Society, 42, 523.Google Scholar
Kim, S. and Cho, M. (2019). New star identification algorithm using labelling technique. Acta Astronaut, 162, 367–372.CrossRefGoogle Scholar
Kumler, J. and Bauer, M. (2000). Fish-eye lens designs and their relative performance. International Symposium on Optical Science & Technology, 4093, 360–369.Google Scholar
Lang, D., Hogg, D. W., Mierle, K., Blanton, M. and Roweis, S. (2010). Blind astrometric calibration of arbitrary astronomical images. The Astronomical Journal, 139, 1782–1800.CrossRefGoogle Scholar
Leake, C., Arnas, D. and Mortari, D. (2020). Non-dimensional star-identification. Sensors, 20, 2697.CrossRefGoogle ScholarPubMed
Li, C. H., Zheng, Y., Zhang, C., Yuan, Y. L., Lian, Y. Y. and Zhou, P. Y. (2014). Astronomical vessel position determination utilizing the optical super wide angle lens camera. Journal of Navigation, 67(4), 633–649.CrossRefGoogle Scholar
Liu, X., Zhou, Z., Zhang, Z., Liu, D. and Zhang, X. (2017). Improvement of star identification based on star trace in star images. Measurement, 105, 158–163.CrossRefGoogle Scholar
Mehta, D. S., Chen, S. and Low, K. S. (2018). A robust star identification algorithm with star shortlisting. Advances in Space Research, 61, 2647–2660.CrossRefGoogle Scholar
Mohamad, J. A., Ghafarzadeh, M., Taheri, M., Mosadeq, E. and Khakian, M. G. (2015). ). star identification algorithm for uncalibrated, wide FOV cameras. The Astronomical Journal, 149, 182–187.Google Scholar
Na, M. and Jia, P. (2006). A survey of all-sky autonomous star identification algorithms. International Symposium on Systems and Control in Aerospace and Astronautics, IEEE, Harbin, China, January 19–21.Google Scholar
Padgett, C. and Kreutz-Delgado, K. (1997). A grid algorithm for autonomous star identification. IEEE Transactions on Aerospace and Electronic Systems, 33(1), 202–213.CrossRefGoogle Scholar
Quan, W., Li, J., Gong, X. and Fang, J. (2015). INS/CNS/GNSS Integrated Navigation Technology. Beijing: Springer Berlin Heidelberg.CrossRefGoogle Scholar
Rijlaarsdam, D., Yous, H., Byrne, J., Oddenino, D., Furano, G. and Moloney, D. (2020). A survey of lost-in-space star identification algorithms since 2009. Sensors, 20, 2579.CrossRefGoogle ScholarPubMed
Rousseau, G., Bostel, J. and Mazari, B. (2005). Star recognition algorithm for APS star tracker: Oriented triangles. IEEE Aerospace and Electronic Systems, 20(2), 27–31.CrossRefGoogle Scholar
Samaan, M. A. and Mortari, D. (2006). Nondimensional star identification for uncalibrated star cameras. The Journal of the Astronautical Sciences, 54(1), 95–111.CrossRefGoogle Scholar
Samaan, M. A., Mortari, D. and Junkins, J. L. (2005). Recursive mode star identification algorithms. IEEE Transactions on Aerospace and Electronic Systems, 41(4), 1246–1254.CrossRefGoogle Scholar
Somayehee, F., Nikkhah, A. A., Roshanian, J. and Salahshoor, S. (2020). Blind star identification algorithm. IEEE Transactions on Aerospace and Electronic Systems, 56(1), 547–557.CrossRefGoogle Scholar
Spratling, B. B. and Daniele, A. (2009). A survey on star identification algorithms. Algorithms, 2, 93–107.CrossRefGoogle Scholar
Sun, L., Jiang, J., Zhang, G. and Wei, X. (2016). A discrete hmm-based feature sequence model approach for star identification. IEEE Sensors Journal, 16(4), 931–940.CrossRefGoogle Scholar
Tao, Y. A., Xi, Z. B., Ping, S. C. and Fei, Y. A. (2019). A global optimization algorithm for laboratory star sensors calibration problem. Measurement, 134, 253–265.Google Scholar
Toloei, A., Zahednamazi, M., Ghasemi, R. and Mohammadi, F. (2020). A comparative analysis of star identification algorithms. Astrophysics and Space Science, 63, 1–9.Google Scholar
Vulfovich, B. and Fogilev, V. (2010). New ideas for celestial navigation in the third millennium. The Journal of Navigation, 63, 373–378.CrossRefGoogle Scholar
Wei, X., Cui, C., Wang, G. and Wan, X. (2019). Autonomous positioning utilizing star sensor and inclinometer. Measurement, 131, 132–142.CrossRefGoogle Scholar
Xiao, G., Li, P., Gao, Y. and Heck, B. (2019). A unified model for multi-frequency PPP ambiguity resolution and test results with galileo and BeiDou triple-frequency observations. Remote Sensing, 11, 116.CrossRefGoogle Scholar
Yang, Y., Gao, W., Guo, S., Mao, Y. and Yang, Y. (2019). Introduction to BeiDou-3 navigation satellite system. Navigation, 66, 1–12.CrossRefGoogle Scholar
Yang, Y., Mao, Y. and Sun, B. (2020). Basic performance and future developments of BeiDou global navigation satellite system. Satellite Navigation, 1, 1–8. doi: 10.1186/s 43020-019-0006-0CrossRefGoogle Scholar
Yang, Y. X., Liu, L., Li, J. L., Yang, Y. F., Zhang, T. Q., Mao, Y., Sun, B. J. and Ren, X. (2021). Featured services and performance of BDS-3. Chinese Science Bulletin, 66, 2135–2143.Google Scholar
Zhu, H., Liang, B. and Zhang, T. (2018). A robust and fast star identification algorithm based on an ordered set of points map. Acta Astronaut, 148, 327–336.CrossRefGoogle Scholar