Sparse least mean fourth filter with zero-attracting ℓ1-norm constraint

Guan Gui, Fumiyuki Adachi

    Research output: Contribution to conferencePaperpeer-review

    17 Citations (Scopus)

    Abstract

    Traditional stable adaptive filter was used normalized least-mean square (NLMS) algorithm. However, identification performance of the traditional filter was especially vulnerable to degradation in low signal-noise-ratio (SRN) regime. Recently, adaptive filter using normalized least-mean fourth (NLMF) is attracting attention in adaptive system identifications (ASI) due to its high identification performance and stability. In the case of sparse system, however, the NLMF filter cannot identify effectively due to the fact that its algorithm neglects the inherent sparse structure. In this paper, we proposed a sparse NLMF filter using zero-attracting ℓ1-norm constraint to exploit the sparsity and to improve the identification performance. Effectiveness of the proposed filter is confirmed from two aspects: 1) stability is derived equivalent to well-known stable NLMS filter; 2) identification performance of the proposed is verified by mean square deviation (MSD) standard in computer simulations. When comparing with conventional adaptive filter, the proposed one can achieve much better identification performance especially in low SNR regime.

    Original languageEnglish
    DOIs
    Publication statusPublished - 2013
    Event9th International Conference on Information, Communications and Signal Processing, ICICS 2013 - Tainan, Taiwan, Province of China
    Duration: 2013 Dec 102013 Dec 13

    Other

    Other9th International Conference on Information, Communications and Signal Processing, ICICS 2013
    Country/TerritoryTaiwan, Province of China
    CityTainan
    Period13/12/1013/12/13

    Keywords

    • Normalized least-mean fourth (NLMF)
    • Normalized least-mean square (NLMS)
    • Sparse system identification
    • adaptive filter

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

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