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Assessment of reduced order Kalman filter for parameter identification in one-dimensional blood flow models using experimental data
This work presents a detailed investigation of a parameter estimation
approach based on the reduced order unscented Kalman filter (ROUKF) in the
context of one-dimensional blood flow models. In particular, the main aims of
this study are (i) to investigate the effect of using real measurements vs.
synthetic data (i.e., numerical results of the same in silico model,
perturbed with white noise) for the estimation and (ii) to identify potential
difficulties and limitations of the approach in clinically realistic
applications in order to assess the applicability of the filter to such
setups. For these purposes, our numerical study is based on the in vitro
model of the arterial network described by [Alastruey et al. 2011, J.
Biomech. 44], for which experimental flow and pressure measurements are
available at few selected locations. In order to mimic clinically relevant
situations, we focus on the estimation of terminal resistances and arterial
wall parameters related to vessel mechanics (Youngs modulus and thickness)
using few experimental observations (at most a single pressure or flow
measurement per vessel). In all cases, we first perform a theoretical
identifiability analysis based on the generalized sensitivity function,
comparing then the results obtained with the ROUKF, using either synthetic or
experimental data, to results obtained using reference parameters and to
available measurements