Electrical Capacitance Tomography Sensitivity Field Optimization Algorithm Based on Approximate L0 Norm

Aiming at the under-qualitative problem in solving the inverse problem of electrical capacitance tomography, a sparse regularization algorithm that approximates the L-0 norm is introduced to obtain the sparse solution vector. An iterable sensitivity gradient optimization method of the sensitive field is proposed to address the imaging quality problem caused by the uneven sensitivity distribution of the sensitive field. This method uses the finite elements of the sensitive field as the core to divide the sensitive field into several regions and the data of sensitivity in the region around the core finite element is extracted for mean filtering. And the filtered data is returned to the core finite elements and used as the parameters in the next filtering area. Cyclic filtering can gradually reduce the sensitivity gradient between the center area and edge area of the sensitive field. The sensitivity gradient optimization method is combined with the approximate L, algorithm to verify the feasibility of the proposed algorithm. The results show that compared with the traditional Landweber algorithm, the proposed algorithm reduces the relative error of a reconstructed image to 0.24 and the correlation coefficient to 0.91. The actual static experiment also proves the effectiveness of the proposed algorithm.