Adaptive signal processing
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Step-size control for acoustic echo cancellation filter—an overview
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
A simple variable step size LMS adaptive algorithm: Research Articles
International Journal of Circuit Theory and Applications
A noise resilient variable step-size LMS algorithm
Signal Processing
A stochastic gradient adaptive filter with gradient adaptive stepsize
IEEE Transactions on Signal Processing
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
A new class of gradient adaptive step-size LMS algorithms
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
A class of adaptive step-size control algorithms for adaptivefilters
IEEE Transactions on Signal Processing
A novel kurtosis driven variable step-size adaptive algorithm
IEEE Transactions on Signal Processing
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems
Mathematical and Computer Modelling: An International Journal
On the design of LMS-based channel estimators using the Doppler spread parameter
Digital Signal Processing
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An improved robust variable step-size least mean square (LMS) algorithm is developed in this paper. Unlike many existing approaches, we adjust the variable step-size using a quotient form of filtered versions of the quadratic error. The filtered estimates of the error are based on exponential windows, applying different decaying factors for the estimations in the numerator and denominator. The new algorithm, called more robust variable step-size (MRVSS), is able to reduce the sensitivity to the power of the measurement noise, and improve the steady-state performance for comparable transient behavior, with negligible increase in the computational cost. The mean convergence, the steady-state performance and the mean step-size behavior of the MRVSS algorithm are studied under a slow time-varying system model, which can be served as guidelines for the design of MRVSS algorithm in practical applications. Simulation results are demonstrated to corroborate the analytic results, and to compare MRVSS with the existing representative approaches. Superior properties of the MRVSS algorithm are indicated.