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

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.

摘要

为提高在轨航天器抵御空间碎片撞击的生存能力, 提出一种撞击损伤评估方法。首先, 建立一个针对红外热图像序列数据的多区域损伤挖掘模型, 用于描述处于不同空间层的撞击损伤。采用变分贝叶斯推理来求解模型参数, 从而有效地从红外热图像数据中识别不同类型撞击损伤。然后, 提出一种图像处理框架, 包括具有能量函数的图像分割算法和具有稀疏表示的图像融合方法, 以消除变异贝叶斯误差并比较不同类型损伤的位置。在试验部分, 将上述方法用于评估二次碎片云对Whipple防护结构的复杂撞击损伤。实验结果证明本文提出的方法可以对空间碎片超高速撞击造成的不同类型复杂损伤进行有效识别与评估。