An efficient robust adaptive filtering scheme based on parallel subgradient projection techniques

I. Yamada, K. Slavakis, K. Yamada

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and an extension of the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. The proposed adaptive algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified. The numerical examples show that the proposed adaptive filtering scheme achieves low estimation error and realizes dramatically fast and stable convergence even for highly colored excited input signals in severely noisy situations.

Original languageEnglish (US)
Pages (from-to)3725-3728
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
StatePublished - Sep 26 2001

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