This paper establishes sufficient conditions to bound the error in perturbed conditional mean estimates derived from a perturbed model (only the scalar case is shown in this paper but a similar result is expected to hold for the vector case). The results established here extend recent stability results on approximating information state filter recursions to stability results on the approximate conditional mean estimates. The presented filter stability results provide bounds for a wide variety of model error situations
Develops a framework for state-space estimation when the parameters of the underlying linear model a...
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
We obtain a conditional prediction mean squared error for a state space model with estimated paramet...
This paper establishes practical stability results for an important range of approximate discrete-ti...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
We consider in the following the problem of recursive filtering in linear state-space models. The cl...
This paper establishes practical stability results for an important range of approximate discrete-ti...
International audienceThis article develops a comprehensive framework for stability analysis of a br...
The present paper deals with the problem of reduced complexity model estimation in the framework of ...
Abstract – Applying the Kalman filtering scheme to linearized system dynamics and observation models...
Abstract: The present paper deals with the problem of reduced complexity model estimation in the fra...
This electronic version was submitted by the student author. The certified thesis is available in th...
This paper addresses the stability of a Kalman filter when measurements are intermittently available...
This paper deals with some issues involving a parameter estimation approach that yields estimates co...
Develops a framework for state-space estimation when the parameters of the underlying linear model a...
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
We obtain a conditional prediction mean squared error for a state space model with estimated paramet...
This paper establishes practical stability results for an important range of approximate discrete-ti...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
The aim of this contribution is to investigate the stability of approximate conditional procedures u...
We consider in the following the problem of recursive filtering in linear state-space models. The cl...
This paper establishes practical stability results for an important range of approximate discrete-ti...
International audienceThis article develops a comprehensive framework for stability analysis of a br...
The present paper deals with the problem of reduced complexity model estimation in the framework of ...
Abstract – Applying the Kalman filtering scheme to linearized system dynamics and observation models...
Abstract: The present paper deals with the problem of reduced complexity model estimation in the fra...
This electronic version was submitted by the student author. The certified thesis is available in th...
This paper addresses the stability of a Kalman filter when measurements are intermittently available...
This paper deals with some issues involving a parameter estimation approach that yields estimates co...
Develops a framework for state-space estimation when the parameters of the underlying linear model a...
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
We obtain a conditional prediction mean squared error for a state space model with estimated paramet...