Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Zhenzhong Song
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, College of Engineering, Institute of Precision Machinery and Smart Structure, Zhejiang Normal University, Jinhua, China
The generalized vector sampled pattern matching (GVSPM) algorithm is widely utilized in the EIT (electrical impedance tomography) reconstruction to solve the ill-posed inverse problem. An improved algorithm, which is called the generalized vector sampled pattern matching-fast (GVSPM-F), is proposed to improve the spatial resolution and reduce the iteration time based on the conventional GVSPM. The GVSPM merely applied the orthogonal projections to approximate the weights in the coordinate directions. The iteration of the proposed GVSPM-F algorithm is calculated in the projection direction of the space constructed by nonlinear correlated column vectors in the column space of the original sensitive matrix. Hence, the proposed GVSPM-F algorithm could achieve stable convergence without an empirical value to preserve the shape information and reduce the time consumption of GVSPM. In the experimental results, GVSPM-F is compared with the traditional GVSPM method in terms of voltage error, iteration time, and image error. The voltage error decreases by approximately 35%, and the number of iteration decreases from thousands to fewer than 100. The image error of GVSPM-F is 6% less than that of GVSPM. The proposed GVSPM-F algorithm is confirmed to be effective for the reconstruction of EIT images.
