A novel robust approach of 3D CNN and SAE-based near-field acoustical holography relying on self-identity constraint data for Kalman gain

For near-field acoustic holography, sparse array measurement for cost reduction can result in inaccuracy due to aliasing error. To attenuate it, there are data-driven methods based on artificial intelligence theories. Among these, the JTCSA-NAH method has not adopted measures for robustness enhancement despite its high accuracy in practice. In this work, the influence of measuring noise on JTCSA-NAH is analyzed followed by the principle of adding Gaussian noise for robustness improvement. Based on the relevant prior conditions, the ICCSA-NAH method, which relies on self-identity constraint data working as the Kalman gain is proposed. Subsequently, numerical example and experiment are carried out, and the results show that compared with JTCSA-NAH method, the mean errors of near-field vibration velocity reconstruction are theoretically and experimentally reduced from 15.19% and 23.64% to 6.03% and 12.45%, respectively, by the ICCSA-NAH method, which verifies the feasibility and superiority of the proposed method.