Why the stochastic MV-PURE estimator excels in highly noisy situations?

Author

Piotrowski, Tomasz ; Yamada, Isao

Author_Institution

Dept. of Commun. & Integrated Syst., Tokyo Inst. of Technol., Tokyo

fYear

2009

fDate

19-24 April 2009

Firstpage

3081

Lastpage

3084

Abstract

The stochastic MV-PURE estimator has recently emerged as the robust solution for frequently occuring in practice problem of linear estimation in ill-conditioned and imperfectly known linear stochastic model. In this paper we provide theoretical results showing that the stochastic MV-PURE estimator can be used to the greatest effect in highly noisy settings. In such settings, we discuss the relation between the stochastic MV-PURE estimator and the well-known reduced rankWiener filter. We verify the theoretical results presented by a means of numerical simulations.

Keywords

Wiener filters; parameter estimation; signal processing; stochastic processes; highly noisy condition; ill-conditioned linear stochastic model; imperfectly known linear stochastic model; linear estimation; minimum-variance pseudounbiased reduced-rank estimator; reduced rank Wiener filter; stochastic MV-PURE estimator; Covariance matrix; Numerical simulation; Parameter estimation; Robustness; Signal processing; Stochastic processes; Stochastic systems; Vectors; Wiener filter; Wireless communication; Stochastic MV-PURE estimator; parameter estimation; reduced-rank estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4960275

Filename

4960275