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

Authors:
Kaushik Mahata;Torsten SöDerströM;Lars HillströM
Affiliations:
Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden;Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden;Alfa Laval Tumba AB, SE-147 80 Tumba, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2004

Citing 3
Cited 1

Markov-based eigenanalysis method for frequency estimation

IEEE Transactions on Signal Processing
Further results and insights on subspace based sinusoidal frequencyestimation

IEEE Transactions on Signal Processing
Non-parametric identification of viscoelastic materials from wave propagation experiments

Automatica (Journal of IFAC)

Brief paper: Bayesian approaches for identification of the complex modulus of viscoelastic materials

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

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.