Steady-state Kalman filtering with nonstationary noise

2003 ◽  
Vol 24 (2) ◽  
pp. 57-71 ◽  
Author(s):  
P. M. Mäkilä ◽  
J. Paattilammi
Keyword(s):  
Steady State ◽  
Kalman Filtering

Steady-state Kalman filtering with an error bound

1989 ◽  
Vol 12 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Dennis S. Bernstein ◽  
Wassim M. Haddad
Keyword(s):  
Steady State ◽  
Error Bound ◽  
Kalman Filtering

On-Line Aircraft Engine Diagnostic Using a Soft-Constrained Kalman Filter

2004 ◽  
Author(s):  
P. Dewallef ◽  
O. Le´onard ◽  
K. Mathioudakis
Keyword(s):  
Steady State ◽  
Parameter Identification ◽  
Kalman Filtering ◽  
Aircraft Engine ◽  
Turbofan Engine ◽  
Engine Model ◽  
On Line ◽  
Typical Component ◽  
Stability And Accuracy ◽  
Component Faults

The purpose of this contribution is to apply ridge regression to Kalman filtering in order to stabilize a health parameter identification under low or negative redundancy. The resulting algorithm achieves a so-called soft-constrained recursive health parameter identification, i.e. constraints are applied to parameters in a statistical way, contrary to hard-constrained algorithms based on strong equality or inequality constrains. The method is tested on data generated by a steady state turbofan engine model and representing typical component faults. The benefits that can be realized in terms of stability and accuracy are highlighted and some limits of the method are also mentioned.

New approach to information fusion steady-state Kalman filtering

Automatica ◽  
2005 ◽  
Vol 41 (10) ◽  
pp. 1695-1707 ◽  
Author(s):  
Zi-Li Deng ◽  
Yuan Gao ◽  
Lin Mao ◽  
Yun Li ◽  
Gang Hao
Keyword(s):  
Steady State ◽  
Kalman Filtering ◽  
Information Fusion ◽  
New Approach
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