Computationally efficient estimation of wave propagation functions from 1-D wave experiments on viscoelastic materials

Least-squares based non-parametric estimation of the wave propagation functions of a viscoelastic material is considered in this paper. Widely used nonlinear least-squares-based algorithms are often computationally expensive and suffer from numerical problems. In this paper, we propose a class of subspace estimators which assume equidistant sensor configuration. The proposed estimator is computationally economical and numerically robust. Analytical expressions for the estimation accuracy have been derived. It is also shown that the subspace estimator achieves the optimal accuracy under the optimal weighting. The algorithm is employed on simulated data as well as on real experimental data. The results therefrom are shown to confirm the analytical results.

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