Hankel-Based Spectral Method for Quantitative Acoustic Microscopy

This work introduces a novel spectral-based framework for quantitative acoustic microscopy (QAM) applications. The proposed approach leverages Hankel (HK) matrix theory and an adaptive least squares method with an alternating direction method of multipliers. Simulation results and real-world experiments conducted with a 500-MHz QAM system demonstrate the robustness and efficiency of the proposed method. Comparative analysis with traditional autoregressive-based method shows a better accuracy in spectral and acoustic parameter estimation. Additionally, the HK-based method exhibits reduced variance in the presence of data corruptions, highlighting its potential for more reliable estimation.