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首页> 外文期刊>Communications in Numerical Methods in Engineering >Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data
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Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data

机译：使用实验数据评估降阶无味卡尔曼滤波器以在一维血流模型中进行参数识别

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摘要

This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall 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 on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements.
机译：这项工作提出了基于一维血流模型的降阶无味卡尔曼滤波器（ROUKF）的参数估计方法的详细研究。尤其是，这项研究的主要目的是（1）研究使用实际测量值与合成数据进行估计程序（即，同一计算机模型的数值结果受到噪声干扰）的影响，以及（2）识别该方法在临床实际应用中可能存在的困难和局限性，以评估过滤器对此类设置的适用性。为了这些目的，本数值研究是基于最近公布的动脉网络体外模型，为此，在选定的几个位置可以进行实验流量和压力测量。为了模拟临床相关情况，我们集中在使用很少的实验观察值（每个血管最多单个压力或流量测量）来估计与血管力学相关的末端阻力和动脉壁参数（杨氏模量和壁厚）。在所有情况下，我们首先根据广义灵敏度函数进行理论上的可识别性分析，然后将ROUKF的结果（使用合成或实验数据）与使用参考参数获得的结果进行比较，并与可用的测量结果进行比较。

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