Autonomous navigation technology based on optical image under large dynamic disturbance

被引：0
|
作者：
Li J. [1 ]
Wang D. [1 ]
E W. [1 ]
Ge D. [1 ]
机构：
[1] Beijing Institute of Spacecraft System Engineering, Beijing
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2021年 / 42卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Aero-optics; Celestial navigation; Hypersonic vehicle; Image restoration; Regularization;
D O I：
10.7527/S1000-6893.2020.24907
中图分类号：
学科分类号：
摘要：
During autonomous navigation in the stratosphere, the hypersonic vehicle will suffer from large dynamic disturbances, such as aero-optical effect and motion blur, which will have great influence on star observation and high-precision navigation. To solve the problem, an improved semi-blind restoration algorithm is proposed based on regularization. Firstly, the priori kernel is extracted by using the existing kernel extraction algorithm for hypersonic star image, so as to provide convenient conditions for image restoration. Then, based on the sparse prior distribution characteristics of gray and gradient of the star image, a regularized blind restoration model for the hypersonic star image is developed. According to the information provided by the prior kernel, an improved semi-blind restoration method is proposed, which can be converted into the blind restoration method when the prior kernel is of low accuracy or is missing, so as to improve the robustness of the algorithm. Numerical simulation results show that compared with the traditional star image restoration algorithm and other latest regularization restoration algorithms, the proposed algorithm has better restoration effect, with PSNR reaching 36.97 and the centroid extraction success rate reaching 99.23%. The success rate of star image recognition can be significantly improved, thus greatly improving the autonomous navigation ability of hypersonic vehicles with large dynamic disturbances in the stratosphere. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
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  • [1] YIN X L., Principle of aero-optics, (2003)
  • [2] POND J E, SUTTON G W., Aero-optic performance of an aircraft forward-facing optical turret, Journal of Aircraft, 43, 3, pp. 600-607, (2006)
  • [3] WHITE M, VISBAL M., Aero-optics of compressible boundary layers in the transonic regime, 43rd AIAA Plasmadynamics and Lasers Conference, (2012)
  • [4] KAMEL M S, WANG K, WANG M., Predictions of aero-optical distortions using LES with wall modeling, 54th AIAA Aerospace Sciences Meeting, (2016)
  • [5] GORDEYEV S, CRESS J A, SMITH A, Et al., Aero-optical measurements in a subsonic, turbulent boundary layer with non-adiabatic walls, Physics of Fluids, 27, 4, (2015)
  • [6] HONG H Y, ZHANG T X., Restoration of turbulence-degraded images using anisotropic and nonlinear regularization, Journal of Astronautics, 25, 1, pp. 5-12, (2004)
  • [7] FERGUS R, SINGH B, HERTZMANN A, Et al., Removing camera shake from a single photograph, ACM Transactions on Graphics, 25, 3, pp. 787-794, (2006)
  • [8] KRISHNAN D, FERGUS R., Fast image deconvolution using hyper-Laplacian priors, International Conference on Neural Information Processing Systems, (2011)
  • [9] LI W H, LI Q L, GONG W G, Et al., Total variation blind deconvolution employing split Bregman iteration, Journal of Visual Communication and Image Representation, 23, 3, pp. 409-417, (2012)
  • [10] OHKOSHI K, GOTO T, HIRANO S, Et al., Blind image restoration based on total variation regularization of blurred images, The 1st IEEE Global Conference on Consumer Electronics, pp. 621-622, (2012)
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