An improved block orthogonal matching pursuit for moving force identification using block-sparse compressed sensing

Moving force identification (MFI) based on compressed sensing (CS) has been proven to be promising in bridge structural health monitoring. Nevertheless, there exist the difficulties in storage and transmission cost of massive monitoring data and the lack of considering the block-sparse features of moving forces in particular transform domains. Inspired by the structural characteristics of sparse representation data, a block-sparse compressed sensing (BSCS) framework is first developed to reconstruct moving forces with block-sparse distribution in the frequency domain. To solve the BSCS equation, an improved block orthogonal matching pursuit algorithm, considering atom screening strategy (ASS-BOMP), is then proposed to ensure that moving forces can still be successfully identified even if the preset block length is inconsistent with the block-sparse features of moving forces. The optimal coherence threshold parameters in the atom screening strategy are determined by the Bayesian information criterion (BIC). Finally, the accuracy and feasibility of the proposed method are validated through numerical simulations and laboratory experiments. The results show that the proposed method can effectively compress response data and accurately identify moving forces distributed in the form of frequency band. Furthermore, the ASS-BOMP algorithm outperforms the traditional BOMP with higher MFI accuracy and robustness.

Automated summary

This paper proposes a block-sparse compressed sensing framework for the identification of moving forces, which can reconstruct the forces in the frequency domain. The improved block orthogonal matching pursuit algorithm enhances the accuracy and robustness of the identification process.