Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T05:52:50.426Z Has data issue: false hasContentIssue false
  • English
  • Français

SIMU/Triple star sensors integrated navigation method of HALE UAV based on atmospheric refraction correction

Published online by Cambridge University Press:  08 February 2022

Ziqian Gao Open the ORCID record for Ziqian Gao [Opens in a new window] ,
Haiyong Wang ,
Weihong Wang  and
Yuan Xu
Ziqian Gao
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, China.
Haiyong Wang*
Affiliation:
School of Astronautics, Beihang University, Beijing, China.
Weihong Wang
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, China.
Yuan Xu
Affiliation:
Shandong Institute of Space Electronic Technology, Yantai, China
*
*Corresponding author. E-mail: why@buaa.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

To achieve autonomous all-day flight by high-altitude long-endurance unmanned aerial vehicle (HALE UAV), a new navigation method with deep integration of strapdown inertial measurement unit (SIMU) and triple star sensors based on atmospheric refraction correction is proposed. By analysing the atmospheric refraction model, the stellar azimuth coordinate system is introduced and the coupling relationship between attitude and position is established. Based on the geometric relationship whereby all the stellar azimuth planes intersect on the common zenith direction, the sole celestial navigation system (CNS) method by stellar refraction with triple narrow fields of view (FOVs) is studied and a loss function is built to evaluate the navigation accuracy. Finally, the new SIMU/triple star sensors deep integrated navigation method with refraction correction upgraded from the traditional inertial navigation system (INS)/CNS integrated method can be established. The results of simulations show that the proposed method can effectively restrain navigation error of a HALE UAV in 24 h steady-state cruising in the stratosphere.

Keywords

atmospheric refractonHALE UAVtriple-FOV star sensorsintegrated navigation
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 3 , May 2022 , pp. 704 - 726
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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

Chapman, D., Aboobaker, A. M., Araujo, D., Didier, J., Grainger, W., Hanany, S., Hillbrand, S., Limon, M., Miller, A., Reichborn-Kjennerud, B., Sagiv, I., Tucker, G. and Vinokurov, Y. (2015). Star Camera System and New Software for Autonomous and Robust Operation in Long Duration Flights. Aerospace Conference IEEE, Big Sky, MT, 1–11.CrossRefGoogle Scholar
Fan, Q. and Li, X. (2011). Selection of optical-system parameters for an all-day used star sensor. Acta Optica Sinica, 31(11), 254–260.Google Scholar
He, Z., Wang, X. and Fang, J. (2014). An innovative high-precision SINS/CNS deep integrated navigation scheme for the Mars rover. Aerospace Science & Technology, 39, 559–566.CrossRefGoogle Scholar
Ho, K. (2012). A survey of algorithms for star identification with low-cost star trackers. Acta Astronautica, 73, 156–163.CrossRefGoogle Scholar
Honda, M., Athar, M. S., Kajita, T., Kasahara, K. and Midorikawa, S. (2015). Atmospheric neutrino flux calculation using the NRLMSISE-00 atmospheric model. Physical Review D, 92(2), 023004.CrossRefGoogle Scholar
Kurzke, J. and Halliwell, I. (2018). Gas Properties and Standard Atmosphere. In: Propulsion and Power. An Exploration of Gas Turbine Performance Modeling. Cham: Springer, 613–618.CrossRefGoogle Scholar
Li, B., Sun, Q. and Zhang, T. (2015). A star pattern recognition algorithm for the double-FOV star sensor. IEEE Aerospace & Electronic Systems Magazine, 30(8), 24–31.CrossRefGoogle Scholar
Li, P., Sheng, G., Zhang, X., Wu, J., Xu, B., Liu, X. and Zhang, Y. (2018). Underwater terrain-aided navigation system based on combination matching algorithm. ISA Transactions, 78, 80–87.CrossRefGoogle ScholarPubMed
Liu, J., Feng, Y. and Zou, L. (2018). Some three-term conjugate gradient methods with the inexact line search condition. Calcolo, 55(2), 16.CrossRefGoogle Scholar
Ning, X. and Wang, L. (2013). Method of high accuracy celestial altitude obtainment in vessel navigation. Acta Optica Sinica, 33(3), 17–25.Google Scholar
Ning, X., Zhang, J., Gui, M. and Fang, J. (2018). A fast calibration method of the star sensor installation error based on observability analysis for the tightly coupled SINS/CNS integrated navigation system. IEEE Sensors Journal, 18(16), 6794–6803.CrossRefGoogle Scholar
Nocedal, J. and Wright, S. J. (2006). Numerical Optimization. New York: Springer.Google Scholar
Pan, Q., Cheng, Y., Liang, Y., Yang, F. and Wang, X. (2013). Multi-Source Information Fusion Theory and Its Applications. Beijing: Tsinghua University Press.Google Scholar
Qin, Y. (2006). Inertial Navigation. Beijing: Science Press.Google Scholar
Rex, M., Chapin, E., Devlin, M. J., Gundersen, J., Klein, J., Pascale, E. and Wiebe, D. (2006). BLAST autonomous daytime star cameras. Proceedings of SPIE - The International Society for Optical Engineering, 6269, 1–8.Google Scholar
Rivaie, M., Mamat, M. and Abashar, A. (2015). A new class of nonlinear conjugate gradient coefficients with exact and inexact line searches. Applied Mathematics and Computation, 268, 1152–1163.CrossRefGoogle Scholar
Shuster, M. D. and Oh, S. D. (1981). Three-axis attitude determination from vector observations. Journal of Guidance and Control, 4(1), 70–77.CrossRefGoogle Scholar
The Purple Mountain Observatory, Chinese Academy of Sciences. (2021). Chinese Astronomical Almanac 2021. Beijing: Science Press.Google Scholar
Wang, H., Lin, H. and Zhou, W. (2011). Technology of atmospheric refraction compensation in starlight observation. Acta Optica Sinica, 31(11), 7–12.Google Scholar
Wang, K., Zhu, T., Qin, Y., Zhang, C. and Li, Y. (2017a). Integration of star and inertial sensors for spacecraft attitude determination. Journal of Navigation, 70(6), 1–14.CrossRefGoogle Scholar
Wang, W., Wei, X., Li, J. and Wang, G. (2017b). Noise suppression algorithm of short-wave infrared star image for daytime star sensor. Infrared Physics & Technology, 85, 382–394.CrossRefGoogle Scholar
Wang, H., Gao, Z. and Wang, K. (2021a). An accuracy evaluation method of stellar navigation for aircraft based on atmospheric refraction. Chinese Patent, 202110176450.6.Google Scholar
Wang, H., Hua, W., Xu, H. and Xu, Y. (2021b). Centroiding method for star image spots under interference of Sun straylight noise in a star sensor. Acta Optica Sinica, 41(03), 143–151.Google Scholar
Wu, L., Wang, J. and Wang, H. (2015). Guide star selection for star pattern recognition between three FOVs. Optics & Precision Engineering, 23(6), 1732–1741.Google Scholar
Wu, L., Xu, Q., Wang, H., Lyu, H. and Li, K. (2019). Guide star selection for the three-FOV daytime star sensor. Sensors, 19(6), 1457.CrossRefGoogle ScholarPubMed
Yu, Y., Cao, J., Tang, Z., Luo, H. and Zhao, M. (2015). Differential measurement of atmospheric refraction using a telescope with double fields of view. Research in Astronomy and Astrophysics, 15(10), 1742–1750.CrossRefGoogle Scholar
Zheng, X., Huang, Y., Mao, X., He, F. and Ye, Z. (2020). Research Status and Key Technologies of All-day Star Sensor. Journal of Physics Conference Series - 2019 The 10th Asia Conference on Mechanical and Aerospace Engineering, 1510, 012027.CrossRefGoogle Scholar
Zhu, X., Ma, M., Cheng, D. and Zhou, Z. (2017). An optimized triad algorithm for attitude determination. Artificial Satellites, 52(3), 41–47.CrossRefGoogle Scholar
Zhu, J., Wang, X., Li, H., Che, H. and Li, Q. (2018). A high-accuracy SINS/CNS integrated navigation scheme based on overall optimal correction. Journal of Navigation, 71, 1567–1588.CrossRefGoogle Scholar