Spectrum estimation of real vector wide sense stationary processes by the hybrid steepest descent method

Konstantinos Slavakis, Isao Yamada, Kohichi Sakaniwa

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

It is well-known that the unbiased estimate of the covariance matrix of a real vector wide sense stationary process is not necessarily positive semidefinite. By defining the real Hilbert space of all symmetric matrices, the conditions for a symmetric matrix to be positive definite, block Toeplitz, as well as to satisfy other design constraints, are formed as closed convex sets. This paper demonstrates that the problem of approximating the unbiased estimate of the covariance matrix of a real vector wide sense stationary process over the intersection of those closed convex sets in an optimal way can be resolved by the Hybrid Steepest Descent Method. An optimal solution is also provided even when inconsistent constraints are met, i.e., whenever the intersection of the closed convex sets is empty. The numerical results exhibit significant improvement of the proposed method over the standard estimates of the covariance matrix.

Original languageEnglish (US)
Pages (from-to)II/1357-II/1360
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
DOIs
StatePublished - 2002
Event2002 IEEE International Conference on Acoustic, Speech and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

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