Schäfer M, Rabenstein R, Ruderer A (2019)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Pages Range: 3786 - 3789
DOI: 10.23919/ecc.2019.8795692
The behavior of a distributed parameter system can be represented by an expansion into eigenfunctions. It allows to calculate the output signal in dependence on the parameters of the system. This contribution considers the inverse problem: Estimate the system parameters from noisy measurements of the output. To this end, an eigenfunction expansion serves to establish a state-space description of the distributed parameter system. The corresponding state-space matrices define an extended Kalman filter to perform estimation and tracking of parameter values. This approach is shown here for a diffusion problem in one spatial dimension. The value of the diffusion parameter is estimated from a simulated particle concentration under varying conditions. Problems of this kind arises for example in the emerging field of molecular communications.
APA:
Schäfer, M., Rabenstein, R., & Ruderer, A. (2019). An Eigenfunction Approach to Parameter Estimation for 1D Diffusion Problems. In Proceedings of the European Control Conference (ECC) (pp. 3786 - 3789). Neapel, IT.
MLA:
Schäfer, Maximilian, Rudolf Rabenstein, and Alexander Ruderer. "An Eigenfunction Approach to Parameter Estimation for 1D Diffusion Problems." Proceedings of the European Control Conference (ECC), Neapel 2019. 3786 - 3789.
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