Sparse vibration sensing for full-field response reconstruction of large-scale satellite structures using optimal sensor placement

Accurate reconstruction of vibration responses at critical locations, such as camera mounting points, is essential for the structural health monitoring (SHM) and performance assurance of remote sensing satellites. However, direct measurements are often infeasible due to spatial constraints, making it necessary to recover full-field responses from a limited number of sensors. Since reconstruction accuracy strongly depends on the number and spatial distribution of sensors, predefined sensor layouts or random sampling strategies may not guarantee optimal reconstruction performance under a limited sensing budget. To address this challenge, this study proposes a sparse vibration sensing (SVS) framework for full-field response reconstruction using optimal sensor placement (OSP), in which sensor configurations are determined by directly enhancing the reconstruction capability of structural responses. A unified optimization criterion is formulated by integrating reconstruction accuracy and sensing-matrix coherence, enabling the selection of sensor locations that effectively capture the dominant dynamic characteristics of the structure. The proposed approach is validated through a high-fidelity finite element model (FEM) of a satellite support structure with more than 16,000 degrees of freedom, as well as experimental tests on a physical specimen. The results demonstrate that the proposed method significantly outperforms conventional sensor placement approaches, achieving high reconstruction accuracy with a limited number of sensors. In addition, the method shows strong robustness against measurement noise and variations in sparse representation. These findings indicate that the proposed framework provides an effective and practical solution for SVS and OSP in large-scale structural systems, with potential applications in aerospace structural monitoring.