A Survey of Global Information Fusion Optimization Algorithms Based on Distributed Precise Data

Electrical Capacitance Tomography (ECT) is a prominent research topic in the field of electrical imaging techniques. However, the accuracy of ECT image reconstruction is limited due to the inherent “soft field” characteristics and ill-posed nature of ECT systems. To address this issue and enhance the precision of ECT reconstruction, we propose an optimization method that leverages the fusion of distributed and accurate data. Specifically, a selected set of precise data from known positions is utilized, and comparative simulations are performed with various imaging algorithms. The experimental findings demonstrate that the data fusion optimization algorithm based on distributed accurate data yields improved image reconstruction quality compared to conventional approaches. By capitalizing on the complementary attributes of both field data and point data, the proposed data fusion algorithm enhances the quality of reconstructed images, enabling the visualization of gas-solid two-phase flow distribution. These results demonstrate the feasibility and efficacy of the point data-based data fusion algorithm, which we believe contributes to the advancement of ECT image reconstruction techniques.