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CLC number: TP301

On-line Access: 2022-04-20

Received: 2020-10-25

Revision Accepted: 2022-05-04

Crosschecked: 2021-02-22

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Xuegang HUANG


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.530-541


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

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

Affiliation(s):  Hypervelocity Aerodynamics Institute, China Aerodynamics Research and 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, 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.

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%T Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact
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%A Anhua SHI
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A1 - Xuegang HUANG
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A1 - Qing LUO
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J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000575

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Chen SY, Cheng ZY, Liu C, 2019. A blind stopping condition for orthogonal matching pursuit with applications to compressive sensing radar. Signal Process, 165:331-342.

[2]Gao B, Woo WL, Tian GY, et al., 2016a. Electromagnetic thermography nondestructive evaluation: physics-based modeling and pattern mining. Sci Rep, 6:25480.

[3]Gao B, Woo WL, He YZ, et al., 2016b. Unsupervised sparse pattern diagnostic of defects with inductive thermography imaging system. IEEE Trans Ind Inform, 12(1):371-383.

[4]Geng XR, Ji LY, Sun K, 2016. Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data. Front Inform Technol Electron Eng, 17(5):403-412.

[5]Guo ZB, Zhang Y, 2019. A sparse corruption non-negative matrix factorization method and application in face image processing & recognition. Measurement, 136:429-437.

[6]Huang XG, Yin C, Ru HQ, et al., 2020. Hypervelocity impact damage behavior of B4C/Al composite for MMOD shielding application. Mater Des, 186:108323.

[7]Kang B, Zhu WP, Liang D, et al., 2019. Robust visual tracking via nonlocal regularized multi-view sparse representation. Patt Recogn, 88:75-89.

[8]Kokkinos Y, Margaritis KG, 2018. Managing the computational cost of model selection and cross-validation in extreme learning machines via Cholesky, SVD, QR and eigen decompositions. Neurocomputing, 295:29-45.

[9]Kullback S, Leibler RA, 1951. On information and sufficiency. Ann Math Stat, 22(1):79-86.

[10]Li X, Sun J, Xiao F, 2016. An efficient prediction framework for multi-parametric yield analysis under parameter variations. Front Inform Technol Electron Eng, 17(12):1344-1359.

[11]Liou JC, Johnson NL, 2006. Risks in space from orbiting debris. Science, 311(5759):340-341.

[12]Ma XL, Hu SH, Liu SQ, et al., 2018. Multi-focus image fusion based on joint sparse representation and optimum theory. Signal Process Image Commun, 78:125-134.

[13]Peng YG, Ganesh A, Wright J, et al., 2012. RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Trans Patt Anal Mach Intell, 34(11):2233-2246.

[14]Sasmaz E, Mingle K, Lauterbach J, 2015. High-throughput screening using Fourier-transform infrared imaging. Engineering, 1(2):234-242.

[15]Sun J, Chen QD, Sun JN, 2019. Graph-structured multitask sparsity model for visual tracking. Inform Sci, 486:133-147.

[16]Wang ZY, Zhu R, Fukui K, 2018. Cone-based joint sparse modelling for hyperspectral image classification. Signal Process, 144:417-429.

[17]Wu T, Shi J, Jiang XM, et al., 2018. A multi-objective memetic algorithm for low rank and sparse matrix decomposition. Inform Sci, 468:172-192.

[18]Yang Y, Cong XC, Long KY, et al., 2018. MRF model-based joint interrupted SAR imaging and coherent change detection via variational Bayesian inference. Signal Process, 151:144-154.

[19]Yin C, Xue T, Huang XG, et al., 2019. Research on damages evaluation method with multi-objective feature extraction optimization scheme for M/OD impact risk assessment. IEEE Access, 7:98530-98545.

[20]Zhang HN, Huang XG, Yin C, et al., 2020. Design of hypervelocity-impact damage evaluation technique based on Bayesian classifier of transient temperature attributes. IEEE Access, 8:18703-18715.

[21]Zong JJ, Qiu TS, Li WS, 2019. Automatic ultrasound image segmentation based on local entropy and active contour model. Comput Math Appl, 78(3):929-943.

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