2-Dimensional Variable Step-Size Sequential Adaptive Gradient Algorithms With Applications

    Authors

    W. B. Mikhael;S. M. Ghosh

    Comments

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    Abstract

    This paper develops the optimality criterion governing the choice of the convergence factor for the two-dimensional sequential adaptive gradient algorithms. Two two-dimensional variable step-size sequential algorithms satisfying the proposed optimality constraint are derived and investigated. These are the two-dimensional individual adaptation (TDIA) algorithm and the two-dimensional homogeneous adaptation (TDHA) algorithm. The TDIA algorithm uses optimal convergence factors tailored for each two-dimensional adaptive filter coefficient at each iteration. The TDHA algorithm uses the same convergence factor for all the filter coefficients, but the convergence factor is optimally updated at each iteration. Neither algorithm requires any a priori knowledge about the statistics of the system signals. In addition, the convergence factors are easily obtained from readily available signals without any differentiation or matrix inversions. The convergence characteristics and adaptation accuracy are greatly improved at the expense of a modest increase in computational complexity in comparison with fixed step-size LMS algorithms. This is verified using computer simulations in system identification and noise cancellation applications.

    Journal Title

    Ieee Transactions on Circuits and Systems

    Volume

    38

    Issue/Number

    12

    Publication Date

    1-1-1991

    Document Type

    Letter

    Language

    English

    First Page

    1577

    Last Page

    1580

    WOS Identifier

    WOS:A1991GY19800021

    ISSN

    0098-4094

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