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

Authors:
Tomasz Piotrowski;Isao Yamada
Affiliations:
Department of Communications and Integrated Systems (S3-60), Tokyo Institute of Technology, 152-8552, Japan;Department of Communications and Integrated Systems (S3-60), Tokyo Institute of Technology, 152-8552, Japan
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Efficient parallel computation of the stochastic MV-PURE estimator by the hybrid steepest descent method

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II

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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.