Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-19T06:48:10.718Z Has data issue: false hasContentIssue false
  • English
  • Français

The Stellar-INS Navigation Performance Influence Mechanism of Star Vector Orientation in the Field of View

Published online by Cambridge University Press:  11 September 2020

Chunxi Zhang ,
Yanqiang Yang ,
Hao Zhang  and
Xiaowen Cai
Chunxi Zhang
Affiliation:
(Institute of Optics and Electronics, School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China)
Yanqiang Yang
Affiliation:
(Institute of Optics and Electronics, School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China)
Hao Zhang
Affiliation:
(Beijing Institute of Astronautical Systems Engineering, Beijing, China)
Xiaowen Cai*
Affiliation:
(Institute of Optics and Electronics, School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The star sensor field of view varies from several arc-minutes to 20 degrees, which directly determines the star vector orientation in the field of view (FOV). Although the relationship between star vector orientation in the FOV and attitude accuracy has been revealed, the influence mechanism of star vector orientation on the integrated navigation performance of a stellar inertial navigation system has not been analysed. In order to improve the integrated accuracy, the main errors such as star sensor installation error, gyro error and initial platform angle error should be estimated online. It is significant to study the influence mechanism of star vector orientation on estimation of the above errors. In this paper, the star sensor sensitivity and the geometry factor are defined to feature the difference between the optical axis direction and the non-optical axis direction. The formulised mechanism and quantification results between star vector orientation and integration attitude and error estimation accuracy are clearly given. Simulation and ground testing were conducted and it was found that the larger the star vector orientation along the optical axis, the better the error estimation accuracy. In contrast, the attitude accuracy is weakly sensitive to the orientation of the star vector in conditions of appropriate posture adjustment and star observation scheme. This conclusion can offer universal guidance for the design and evaluation of stellar inertial navigation systems with narrow field of view or large field of view star sensors.

Keywords

Stellar-INSStar Vector OrientationLFOVNFOVStar Sensor Installation Error
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 234 - 246
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aghaei, M. and Moghaddam, H. A. (2016). Grid star identification improvement using optimization approaches. IEEE Transactions on Aerospace and Electronic Systems, 52(5), 2080–2090.CrossRefGoogle Scholar
AIAA (2000). Stellar-inertial guidance capabilities for advanced ICBM. Guidance and Control Conference, Gatlinburg, TN, USA.Google Scholar
Badshah, K., Yongyuan, Q. and Zhang, J. (2015). SINS/CNS Integration Algorithm and Simulations for Extended Time Flights Using Linearized Kalman Filtering. IEEE International Conference Communication Software Networks. Chengdu, China.CrossRefGoogle Scholar
Guo, J. (2016). The Wavelet Domain Hidden Markov Model Analysis of Ship Attitude Based on Dual Star Sensors. IEEE International Conference on Natural Computation. Zhangjiajie, China.Google Scholar
Huaming, Q., Di, W. and Yonghui W, U. (2019). Filtering algorithm of NFOV star sensor measurement delay. Journal of Beijing University of Aeronautics and Astronautics, 45(2), 234–242.Google Scholar
Lewis, S. W., Hockbruckner, M. and Reeve, J. (1991). Stellar Inertial Navigation Growing with the Times. Upgrading of the LN-20 Integrated Inertial Navigation System. Foot & Ankle International. American Orthopaedic Foot and Ankle Society [and] Swiss Foot and Ankle Society, 32(6), 623.Google Scholar
Li, R., Needelman, D., Fowell, R., Tsao, T. and Wu, Y. W. (2006). Reusable Stellar Inertial Attitude Determination (SIAD) Design for Spacecraft Guidance, Navigation & Control. AIAA Guidance, Navigation, and Control Conference and Exhibit. San Francisco, California.Google Scholar
Liu, H., Li, X. and Tan, J. (2010). Novel approach for laboratory calibration of star tracker. Optical Engineering, 49, 1–9.CrossRefGoogle Scholar
Lu, J., Lei, C., Liang, S. and Yang, Y. (2017). An All-Parameter System-Level Calibration for Stellar-Inertial Navigation System on Ground. IEEE Transactions on Instrumentation and Measurement, 66(8), 1–9.CrossRefGoogle Scholar
Malay, B. P., Gaylor, D. and Davis, G. (2005). Stellar-aided Inertial Navigation Systems for Lunar and Mars Exploration. Flight Mechanics Symposium, NASA Goddard Space Flight Center, Greenbelt, MD.Google Scholar
Nash, J. M. and Wells, G. R. (1975). Platform Alignment Using a Strapdown Stellar Sensor. AIAA Journal, 13(5), 659–664.CrossRefGoogle Scholar
Ning, X., Gui, M., Xu, Y. Z., Bai, X. and Fang, J. (2016). INS/VNS/CNS integrated navigation method for planetary rovers. Aerospace Science and Technology, 48, 102–114.CrossRefGoogle Scholar
Pu, M. B., Li, X. and Guo, Y. H. (2017). Nanoapertures with ordered rotations: symmetry transformation and wide-angle flat lensing. Optics Express, 25(25), 31471.CrossRefGoogle ScholarPubMed
Qin, S., Zhan, D., Zheng, J., Wu, W., Jia, H. and Fu, S. (2016). Dynamic attitude measurement method of star sensor based on gyro's precise angular correlation. United States Patent.Google Scholar
Yang, Y., Zhang, C. and Lu, J. (2010). Distortion Model for Star Tracker. International Symposium on Precision Engineering Measurements & Instrumentation. International Society for Optics and Phonetics. Hangzhou, China.Google Scholar
Yang, Q. Y., Zhang, C. X., Lu, J. Z. and Zhang, H. (2018). Classification of Methods in the SINS/CNS Integration Navigation System. IEEE Access, 6, 3150–3158.Google Scholar
Zhang, H., Niu, Y. and Lu, J. (2017). On-orbit calibration for star sensors without priori information. Optics Express, 25(15), 18393.CrossRefGoogle ScholarPubMed
Zhang, C., Niu, Y., Zhang, H. and Lu, J. (2018a). Optimized star sensors laboratory calibration method using a regularization neural network. Applied Optics, 57, 1067–1074.CrossRefGoogle Scholar
Zhang, S., Xing, F. and Sun, T. (2018b). Novel approach to improve the attitude update rate of a star tracker. Optics Express, 26(5), 5164.CrossRefGoogle Scholar