Spatial resolution enhancement of nearfield acoustic holography using the fully complex extreme learning machine

For the problem of insufficient spatial resolution of nearfield acoustic holography reconstruction under the sparse measurement array condition, a hologram pressure interpolation method based on the fully complex extreme learning machine is proposed. Firstly, the measured complex hologram pressures and the planar coordinates of the corresponding measurement points are combined into the training samples. They are used to train the fully complex extreme learning machine. Then, the planar coordinates of the interpolation points are introduced into the trained machine to generate the corresponding pressures, achieving the interpolation of hologram data. Finally, the interpolated hologram pressures are used to reconstruct. The results are compared with those without the interpolation and with the traditional interpolation. Numerical simulations and experimental results show that compared to the results without the interpolation, the proposed method significantly improves the spatial resolution of the reconstructed results without increasing the microphones. Compared to the interpolation method based on the support vector machine or the traditional extreme learning machine, the proposed method is faster and its reconstructed result is more accurate. The robustness of this method is also verified by adding noise interference.