CLC number: TP301
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-02-22
Cited: 0
Clicked: 7228
Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO. Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 530-541.
@article{title="Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact",
author="Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="4",
pages="530-541",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000575"
}
%0 Journal Article
%T Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact
%A Xuegang HUANG
%A Anhua SHI
%A Qing LUO
%A Jinyang LUO
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 4
%P 530-541
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000575
TY - JOUR
T1 - Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact
A1 - Xuegang HUANG
A1 - Anhua SHI
A1 - Qing LUO
A1 - Jinyang LUO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 530
EP - 541
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000575
Abstract: To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact damage assessment method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the variational Bayesian inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate variational Bayesian errors and compare locations of different damage types. It includes an image segmentation algorithm with an 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 shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by hypervelocity impact, including surface and internal defects.
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