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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000575


Variational Bayesian multi-sparse component extraction for damage reconstruction of space Debris hypervelocity impact


Author(s):  Xuegang HUANG, Anhua SHI, Qin LUO, Jinyang LUO

Affiliation(s):  Hypervelocity Aerodynamics Institute, China Aerodynamics Research & Development Center, Mianyang 621000, China

Corresponding email(s):   emei-126@126.com

Key Words:  Hypervelocity impact, Variational Bayesian, Sparse representation, Damage assessment


Xuegang HUANG, Anhua SHI, Qin LUO, Jinyang LUO. Variational Bayesian multi-sparse component extraction for damage reconstruction of space Debris hypervelocity impact[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Xuegang HUANG, Anhua SHI, Qin LUO, Jinyang LUO",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000575"
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Abstract: 
In order to improve the survivability of orbiting spacecraft against space debris impacts, this paper proposes an impact damage assessment method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on infrared thermal image sequence. Subsequently, to be able to identify different impact damage types from infrared image data effectively, the variational Bayesian inference is used to solve parameters in the model. Then, an image-processing framework for eliminating variational Bayesian errors and comparing locations of different damage types is proposed. It includes an image segmentation algorithm with energy function and an image fusion method with sparse representation. In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shied configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by hypervelocity impact damage, comprising surface and internal defects.

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