NDLI: A transmultiplexer-based adaptive filtering structure using a new adaptation scheme

Content Provider	IEEE Xplore Digital Library
Author	Papoulis, E.V. Stathaki, T.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK (Papoulis, E.V.; Stathaki, T.)
Abstract	A maximally decimated (MD) adaptive filtering structure (AFS) is presented based on perfect reconstruction (PR) filter banks (FBs) and their dual transmultiplexers (TMUXs). The system under identification is modeled as a parallel structure of interpolated FIR (IFIR) filters and its output is analysed with the PR-FB this model results from. The new AFS results then from this IFIR system model and the TMUX corresponding to it. The proposed structure adapts at a low rate a number of coefficients practically equal to the length of the system under identification. Hence, this method is computationally efficient for the large system lengths usually encountered in practice. A new algorithm is presented for the adaptation of the subband filter coefficients employing a new power normalisation (PN) scheme that significantly increases the convergence rate (CR) for colored input processes. Experimental results demonstrate the good performance of the proposed multi-rate (MR) structure using the new adaptive algorithm and the improvement in the CR it offers when is compared to other recently proposed approaches or the full-band nLMS filter.
Sponsorship	IEEE IEEE Circuits & Syst. Soc
File Size	463891
File Format	PDF
ISBN	0780374487
DOI	10.1109/ISCAS.2002.1010158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive filters Finite impulse response filter Signal analysis Filter bank Chromium Adaptive algorithm Convergence Filtering algorithms Power system modeling Educational institutions
Content Type	Text
Resource Type	Article

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