Add to ORKGIn this paper we consider the problem of obtaining a state space realization of a zero mean gaussian vector process. A new algorithm is presented for the case in which the process is given in terms of its spectral density function. Contrary to the usual procedure followed, which requires a partial fraction expansion, the algorithm presented starts with a (deterministic) realization of the spectral density function itself.
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
A new procedure for stochastic realization of spectral density matrices is briefly reviewed. Afterwa...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
A spectral density matrix estimator for stationary stochastic vector processes is studied, As the du...
Abstract---We discuss the problem of generating realizations of length N from a Gaussian stationary ...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
ABSTP CT:K comprehensive theory of stochastic realization for multivariate stationary Gaussian proce...
Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks t...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
The paper reviews several methods for the generation of stationary realizations of sampled time hist...
Abstract—We discuss the problem of generating re-alizations of length N from a Gaussian stationary p...
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
A new procedure for stochastic realization of spectral density matrices is briefly reviewed. Afterwa...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
A spectral density matrix estimator for stationary stochastic vector processes is studied, As the du...
Abstract---We discuss the problem of generating realizations of length N from a Gaussian stationary ...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
ABSTP CT:K comprehensive theory of stochastic realization for multivariate stationary Gaussian proce...
Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks t...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
The paper reviews several methods for the generation of stationary realizations of sampled time hist...
Abstract—We discuss the problem of generating re-alizations of length N from a Gaussian stationary p...
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...