# A Multi-Modal Decision-Level Fusion Framework for Hypervelocity Impact Damage Classification in Spacecraft

## TL;DR

This paper introduces a new framework that combines multiple data sources to accurately detect and classify damage on spacecraft from high-speed impacts.

## Contribution

The novel framework integrates infrared and vibration data with a dual-stream ResNet and D-S theory for decision-level fusion in HVI damage classification.

## Key findings

- The proposed method achieves 99.01% mean accuracy, outperforming unimodal approaches.
- It corrects misclassifications in micro-cracks and perforation with over 96.9% precision.
- The method shows high stability with a standard deviation of 0.74%.

## Abstract

During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage patterns due to mechanical noise and imaging distance variations. To overcome these physical limitations, this study proposes a physics-informed multimodal fusion framework. Innovatively, we integrate a distance-aware infrared enhancement strategy with vibration spectral subtraction to align heterogeneous data qualities while employing a dual-stream ResNet coupled with Dempster–Shafer (D-S) evidence theory to rigorously resolve inter-modal conflicts at the decision level. Experimental results demonstrate that the proposed strategy achieves a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%). Notably, the fusion mechanism corrects specific misclassifications in micro-cracks and perforation, ensuring a precision exceeding 96.9% across all categories with high stability (standard deviation 0.74%). These findings validate the efficacy of multimodal fusion for precise on-orbit damage assessment, offering a robust solution for spacecraft structural health monitoring.