AbstractWe consider a state covariance assignment problem with measurement noise. We derive a unified solution for both the continuous-time and the discrete-time case, based on a symmetric matrix equation of quadratic type. The solution includes (1) the existence condition for a feedback gain which assigns a specified covariance, (2) an explicit parametrization of the entire set of feedback gains that assign a specified covariance. We can show that the previous results for free-measurement noise can be readily derived as special cases
In this thesis, we introduce two different methods for determining noise covariance matrices in orde...
In Kalman filtering applications, the variance of the estimation error is guaranteed to be minimize...
The concept and use of the steady-state Kalman gain in Kalman filtering theory is presented in this ...
AbstractWe consider a state covariance assignment problem with measurement noise. We derive a unifie...
Performance objectives that are expressed as upper bounds on the steady state variances of the syste...
AbstractIn an earlier paper, the conservative and minimal bound to the crosscorrelation terms betwee...
In order to estimate states from a noise-driven state space system, the state estimator requires a p...
International audienceThis paper deals with the covariance control for discrete-time linear switched...
This paper presents a noise covariance estimation method for dynamical models with rectangular noise...
This paper addresses the problem of designing state or observer-based output feedback controllers fo...
International audienceWe prove a new robust stabilization theorem for systems with time-varying dist...
In the state-space approach to modeling vector-valued time series, obtaining a stochastic process mo...
This paper considers state estimation for dynamic systems in the case of nonwhite, mutually correlat...
Abstract. The problem of estimating a spiked covariance matrix in high dimensions under Frobenius lo...
International audienceWe prove a new robust stabilization theorem for time-varying systems that have...
In this thesis, we introduce two different methods for determining noise covariance matrices in orde...
In Kalman filtering applications, the variance of the estimation error is guaranteed to be minimize...
The concept and use of the steady-state Kalman gain in Kalman filtering theory is presented in this ...
AbstractWe consider a state covariance assignment problem with measurement noise. We derive a unifie...
Performance objectives that are expressed as upper bounds on the steady state variances of the syste...
AbstractIn an earlier paper, the conservative and minimal bound to the crosscorrelation terms betwee...
In order to estimate states from a noise-driven state space system, the state estimator requires a p...
International audienceThis paper deals with the covariance control for discrete-time linear switched...
This paper presents a noise covariance estimation method for dynamical models with rectangular noise...
This paper addresses the problem of designing state or observer-based output feedback controllers fo...
International audienceWe prove a new robust stabilization theorem for systems with time-varying dist...
In the state-space approach to modeling vector-valued time series, obtaining a stochastic process mo...
This paper considers state estimation for dynamic systems in the case of nonwhite, mutually correlat...
Abstract. The problem of estimating a spiked covariance matrix in high dimensions under Frobenius lo...
International audienceWe prove a new robust stabilization theorem for time-varying systems that have...
In this thesis, we introduce two different methods for determining noise covariance matrices in orde...
In Kalman filtering applications, the variance of the estimation error is guaranteed to be minimize...
The concept and use of the steady-state Kalman gain in Kalman filtering theory is presented in this ...