Title :
On the partial stochastic realization problem
Author :
Byrnes, Christopher I. ; Lindquist, Anders
Author_Institution :
Dept. of Syst. Sci. & Math., Washington Univ., St. Louis, MO, USA
fDate :
8/1/1997 12:00:00 AM
Abstract :
We describe a complete parameterization of the solutions to the partial stochastic realization problem in terms of a nonstandard matrix Riccati equation. Our analysis of this covariance extension equation (CEE) is based on a complete parameterization of all strictly positive real solutions to the rational covariance extension problem, answering a conjecture due to Georgiou (1987) in the affirmative. We also compute the dimension of partial stochastic realizations in terms of the rank of the unique positive semidefinite solution to the CEE, yielding some insights into the structure of solutions to the minimal partial stochastic realization problem. By combining this parameterization with some of the classical approaches in partial realization theory, we are able to derive new existence and robustness results concerning the degrees of minimal stochastic partial realizations. As a corollary to these results, we note that, in sharp contrast with the deterministic case, there is no generic value of the degree of a minimal stochastic realization of partial covariance sequences of fixed length
Keywords :
Hankel matrices; Jacobian matrices; Riccati equations; Toeplitz matrices; covariance analysis; matrix algebra; maximum entropy methods; polynomials; realisation theory; sequences; signal processing; speech processing; covariance extension equation; existence; nonstandard matrix Riccati; partial stochastic realization problem; robustness; strictly positive real solutions; unique positive semidefinite solution; Entropy; Filters; Impedance matching; Random sequences; Riccati equations; Signal processing; Speech synthesis; Stochastic processes; Stochastic systems; White noise;
Journal_Title :
Automatic Control, IEEE Transactions on