Stochastic Analysis of a Stable Normalized Least Mean Fourth Algorithm for Adaptive Noise Canceling With a White Gaussian Reference

Eweda, E.; Bershad, N.J.

doi:10.1109/tsp.2012.2215607

Cited by 63 publications

(46 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…similar to the stable-NLMF algorithm [24,25]. In practice, in order to avoid a division by zero, we also propose the regularized stable-PNLMF algorithm modifying (6) such that…”

Section: Proportionate Update Approachmentioning

confidence: 99%

“…Hence, we propose the Krylovproportionate normalized least mean mixed norm (KPNLMMN) algorithm having a convex combination of the mean-square and the mean-fourth error objectives. In addition, we point out that the stability of the mean-fourth error based algorithms depends on the initial value of the adaptive filter weights, the input and noise power [23][24][25]. In order to enhance the stability of the introduced algorithms, we further introduce the stable-PNLMF and the stable-KPNLMF algorithms [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we point out that the stability of the mean-fourth error based algorithms depends on the initial value of the adaptive filter weights, the input and noise power [23][24][25]. In order to enhance the stability of the introduced algorithms, we further introduce the stable-PNLMF and the stable-KPNLMF algorithms [24,25]. Finally we provide a complete performance analysis for the introduced algorithms, i.e., the transient, the steady-state and the tracking performance analyses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

et al. 2015

View full text Add to dashboard Cite

a b s t r a c tWe propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e., the transient and the steadystate analyses, for the proportionate update based algorithms, e.g., the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios.

show abstract

“…similar to the stable-NLMF algorithm [24,25]. In practice, in order to avoid a division by zero, we also propose the regularized stable-PNLMF algorithm modifying (6) such that…”

Section: Proportionate Update Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Hence, the steady-state mean square error (MSE) can be derived as Based on the abovementioned independent assumptions and ideal Gaussian noise assumption [13], we can get the following approximations:…”

Section: Nonlinear Sparse Sensingmentioning

confidence: 99%

“…Due to the independence between x m and v(n), {v [13]. Hence, we can also get the following approximations:…”

Section: Nonlinear Sparse Sensingmentioning

confidence: 99%

RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

Gui

Adachi

2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.

show abstract

Nonparametric Variable Step-Size LMAT Algorithm

Guan

Zhi

2016

Circuits Syst Signal Process

View full text Add to dashboard Cite

Stochastic Analysis of a Stable Normalized Least Mean Fourth Algorithm for Adaptive Noise Canceling With a White Gaussian Reference

Cited by 63 publications

References 13 publications

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

Nonparametric Variable Step-Size LMAT Algorithm

Contact Info

Product

Resources

About