A robust variable step-size LMS-type algorithm: analysis and simulations

TitleA robust variable step-size LMS-type algorithm: analysis and simulations
Publication TypeJournal Article
Year of Publication1997
AuthorsAboulnasr, T., and K. Mayyas
JournalSignal Processing, IEEE Transactions on
Pagination631 -639
Date Publishedmar.
Keywordsadaptive signal processing, approximate analysis, convergence of numerical methods, fast convergence, Gaussian noise, least mean squares methods, noise disturbance, nonstationary optimal weight vector, robust variable step-size LMS-type algorithm, signal processing, steady-state performance, time-varying step-size algorithms, time-varying systems, zero-mean stationary Gaussian inputs

A number of time-varying step-size algorithms have been proposed to enhance the performance of the conventional LMS algorithm. Experimentation with these algorithms indicates that their performance is highly sensitive to the noise disturbance. This paper presents a robust variable step-size LMS-type algorithm providing fast convergence at early stages of adaptation while ensuring small final misadjustment. The performance of the algorithm is not affected by existing uncorrelated noise disturbances. An approximate analysis of convergence and steady-state performance for zero-mean stationary Gaussian inputs and for nonstationary optimal weight vector is provided. Simulation results comparing the proposed algorithm to current variable step-size algorithms clearly indicate its superior performance for cases of stationary environments. For nonstationary environments, our algorithm performs as well as other variable step-size algorithms in providing performance equivalent to that of the regular LMS algorithm


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