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.
LI Jiaxing
,
WANG Dayi
,
E Wei
,
GE Dongming
. Autonomous navigation technology based on optical image under large dynamic disturbance[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021
, 42(11)
: 524907
-524907
.
DOI: 10.7527/S1000-6893.2020.24907
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