Analysis of subspace fitting and ML techniques for parameterestimation from sensor array data
IEEE Transactions on Signal Processing
Non-parametric identification of viscoelastic materials from wave propagation experiments
Automatica (Journal of IFAC)
Brief paper: State estimation for linear systems with state equality constraints
Automatica (Journal of IFAC)
A multiple projection approach for constrained system identification
International Journal of Computer Applications in Technology
Differential constraints for bounded recursive identification with multivariate splines
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
The additional information available in the form of linear or nonlinear constraints are often remain unexplored in the parameter identification problems related to linear dynamic systems. Our goal in this work is to explore the knowledge of the linear constraints to achieve significant improvement in the accuracy of the parameter estimates. In the class of problems being addressed here, the unknown boundary conditions appear as nuisance parameters. In practice, these nuisance parameters are eliminated from the loss function to get a variable projection optimization problem in the parameters of interest. In this work, we solve a constrained optimization problem instead, where the additional linear constraints are imposed in the form of partially known boundary conditions. In the process, we show how the accuracy of the estimates is improved by taking the constraints into account. The theoretical methodology is successfully applied also to numerical simulations as well as in real-world experiments.