Mean-Square Performance of a Family of Affine Projection Algorithms

Shin, Hyun-Chool; Sayed, Ali H.

doi:10.1109/tsp.2003.820077

Cited by 292 publications

(237 citation statements)

References 13 publications

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“…The expectation of w(k) 2 Σ ′ is difficult to calculate because of the dependency of Σ ′ on C(k) , Z(k), X(k), and ofw(k) on prior regressors. To solve this problem, we need to use the following independence assumptions [14]:…”

Section: Appendix B Mean-square Stability Analysis Of the Family Of Smentioning

confidence: 99%

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The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

Abadi

Shafiee

2014

Automatika

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Original scientific paperThis paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in the mean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update normalized subband adaptive filter (VSS-SPU-NSAF) is proposed. In this algorithm the filter coefficients are partially updated in each subband at every iteration. Simulation results show the good performance of the proposed algorithms in convergence speed and steady-state MSD.

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Section: Appendix B Mean-square Stability Analysis Of the Family Of Smentioning

confidence: 99%

“…Equation (64) is stable if the matrix G is stable [14]. From (66), the convergence to the mean of the adaptive algorithm in (44) is guaranteed for any µ that satisfies:…”

Section: W(k) Is Independent Of D T (K)x T (K)mentioning

confidence: 99%

The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

Abadi

Shafiee

2014

Automatika

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“…Equation is stable if the matrix G is stable [4]. From (61), we know that 2 =I-μ +μ GMN , where M=E{(n)(n)} XD I+IE{(n)(n)} ⊗⊗ XD…”

Section: Appendix B Mean and Mean-square Stability Analysis Of Thementioning

confidence: 99%

“…In the presence of colored input signals, the LMS and NLMS algorithms have extremely slow convergence rates. To solve this problem a number of adaptive filtering structures based on affine subspace projections [3], [4], [5], data reusing adaptive algorithms [6], [7], [8], Block adaptive filters [2] and multirate techniques have been proposed in the literature [9], [10], [11]. In all these algorithms the selected fixed step-size can change the convergence speed and the steady-state mean square error.…”

Section: Introductionmentioning

confidence: 99%

A Family of Variable Step-Size Affine Projection Adaptive Filtering Algorithms

Abadi¹,

Mehrdad²,

Gholipour³

et al. 2010

IJCEE

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Abstract-This paper presents a family variable step-size (VSS) affine projection (AP) adaptive filtering algorithms with good convergence speed and low steady state mean square error features. In the following, the family of VSS selective partial updates (SPU) affine projection algorithms are established which reduce the computational complexity. The stability bounds of the family of APA and SPU-APA algorithms are analyzed based on the energy conservation arguments. This analysis does not need to assume a Gaussian or white distribution for the regressors. We demonstrate the good performance of the proposed algorithms through simulations in system identification and acoustic echo cancellation scenarios.Index Terms-Adaptive filter, affine projection, selective partial update, variable step-size, stability analysis.

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“…APA is a useful family of adaptive filters whose main purpose is to accelerate the convergence of LMS-type filters, especially for correlated data at a computational cost that is comparable to that of LMS. While LMS updates the weights based on the current input vector and APA updates the weights based on previous input vectors [5]. The algorithm applies to update directions that are orthogonal to the last p input vectors and thus allows decorrelation of an input process, speeding up the convergence [2].…”

Section: Introductionmentioning

confidence: 99%

Absolute Mean Error Based Adaptive Step Size Affine Projection Algorithm for Adaptive Filtering Systems

Jamel¹

2013

IJIEE

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Abstract-This paper presents a new method for adaptive step size for Affine Projection Algorithm (APA). The proposed algorithm is based on an absolute mean of estimation current and prior error vector. It is called Absolute Mean Error Adaptive Step Size Affine Projection Algorithm (AMASSAPA). The main goal of this algorithm is performance enhancement of adaptive colored filtering system in terms of fast convergence time. The proposed adaptive step size method begins the learning process with high learning rate value ( µ MAX ) and then , it decays in an exponential profile to its minimum value (µ MIN ) .The proposed algorithm is tested with a colored input signal and the result shows that it has fast convergence time than traditional APA.Index Terms-Adaptive filter, adaptive step size, affine projection algorithm.

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Mean-Square Performance of a Family of Affine Projection Algorithms

Cited by 292 publications

References 13 publications

The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

A Family of Variable Step-Size Affine Projection Adaptive Filtering Algorithms

Absolute Mean Error Based Adaptive Step Size Affine Projection Algorithm for Adaptive Filtering Systems

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