Recursive identification of certain structured time-varying state–space models

1997 ◽  
Vol 144 (5) ◽  
pp. 489-497 ◽  
Author(s):  
M.H. Moazzam ◽  
D.J. Clements ◽  
T. Hesketh
Keyword(s):  
State Space ◽  
State Space Models ◽  
Recursive Identification ◽  
Time Varying

scholarly journals Time-varying state correlations in state space models and their estimation via indirect inference

2021 ◽  
Author(s):  
Caterina Schiavoni ◽  
Siem Jan Koopman ◽  
Franz C. Palm ◽  
Stephan Smeekes ◽  
Jan van den Brakel
Keyword(s):  
State Space ◽  
State Space Models ◽  
Indirect Inference ◽  
Time Varying

scholarly journals JOINT ESTIMATION OF STATES AND PARAMETERS OF LINEAR SYSTEMS WITH PARAMETER FAULTS UNDER NON-GAUSSIAN NOISES

2020 ◽  
Vol 18 (2) ◽  
pp. 113
Author(s):  
Vladimir Stojanović ◽  
Dragan Pršić ◽  
Ljubiša Dubonjić
Keyword(s):  
State Space ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Linear Time ◽  
Practical Importance ◽  
Joint Estimation ◽  
State Space Models ◽  
Time Varying ◽  
Linear State ◽  
Non Gaussian

Joint estimation of states and time-varying parameters of linear state space models is of practical importance for the fault diagnosis and fault tolerant control. Previous works on this topic consider the joint estimation in the Gaussian noise environment, but not in the presence of outliers. The known fact is that the measurements have inconsistent observations with the largest part of the observation population (outliers). They can significantly make worse the properties of linearly recursive algorithms which are designed to work in the presence of Gaussian noises. This paper proposes the strategy of the joint parameter-state robust estimation of linear state space models in the presence of non-Gaussian noises. The case of parameter-dependent matrices is considered. Because of its good features in robust filtering, the extended Masreliez-Martin filter represents a cornerstone for realization of the robust algorithms for joint state-parameter estimation of linear time-varying stochastic systems in the presence of non-Gaussian noises. The good features of the proposed robust algorithm for joint estimation of linear time-varying stochastic systems are illustrated by intensive simulations.

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