Estimating Parameters and Refining Thermal Models by Using the Extended Kalman Filter Approach

2004 ◽  
Vol 126 (5) ◽  
pp. 809-817 ◽  
Author(s):  
Ashley F. Emery
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Deterministic Model ◽  
Dimensional System ◽  
Time Behavior ◽  
State Variables ◽  
Physical Processes ◽  
One Dimensional ◽  
Full Knowledge ◽  
Filter Approach

Parameter estimation is based upon a comparison of predicted deterministic model responses to data. The models are often numerical, e.g., finite volume, with intrinsic inaccuracies. In addition, the models typically assume a full knowledge of the physical processes. By using the concept of state variables and employing the extended Kalman filter approach it is possible to include additional effects in the model to achieve better agreement between the model and the data. This paper describes such an approach to the estimation of thermal conductivity in a transiently heated and cooled one-dimensional system and shows that it leads to a resolution of questions about the time behavior of the residuals previously observed in an estimation based upon the least squares analysis.

Estimation of Multi-Parameter Models by Least Squares and Adaptive Filters

2002 ◽  
Author(s):  
A. F. Emery
Keyword(s):  
Least Squares ◽  
Adaptive Filters ◽  
Deterministic Model ◽  
Dimensional System ◽  
Time Behavior ◽  
State Variables ◽  
Physical Processes ◽  
One Dimensional ◽  
Full Knowledge ◽  
Filter Approach

Parameter estimation is based upon a comparison of predicted deterministic model responses to data. The models are often numerical, e.g., finite volume, with intrinsic inaccuracies. In addition, the models typically assume a full knowledge of the physical processes. By using the concept of state variables and employing the Kalman filter approach it is possible to include undetermined effects in the model. This paper describes such an approach to the estimation of thermal conductivity in a transiently heated and cooled one dimensional system and shows that it leads to a resolution of questions about the time behavior of the residuals previously observed in an estimation based upon the least squares analysis.

Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement

2021 ◽  
pp. 1-1
Author(s):  
Behzad Behdani ◽  
Mohsen Tajdinian ◽  
Mehdi Allahbakhshi ◽  
Marjan Popov ◽  
Miadreza Shafie-khah ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Geomagnetically Induced Currents ◽  
Induced Currents ◽  
Filter Approach

scholarly journals Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

2019 ◽  
Vol 39 (4) ◽  
pp. 835-849 ◽  
Author(s):  
Jinshan Huang ◽  
Xianzhi Li ◽  
Xiongjun Yang ◽  
Zhupeng Zheng ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Structural Parameters ◽  
Wide Band ◽  
Unknown Inputs ◽  
Filter Methods ◽  
Filter Approach ◽  
Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

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