A Family Of Affine Projection Adaptive Filtering Algorithms With Selective Regressors (original) (raw)

A Family of Selective Partial Update Affine Projection Adaptive Filtering Algorithms

iranian journal of electrical and electronic engineering, 2009

In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms are established by this general formalism. In these algorithms, the filter coefficients are partially updated rather than the entire filter coefficients at every iteration which is computationally efficient. Following this, the transient and steady-state performance analysis of this family of adaptive filter algorithms are studied. This analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate the performance of the presented algorithms through simulations in system identification and acoustic echo cancel...

Affine projection and recursive least squares adaptive filters employing partial updates

2004

We present order K afne projection and recursive least squares adaptive lters employing partial update schemes. The starting point of the work is the MMax tap-selection criterion in which, given a lter length L, only M coefcients are updated that correspond to the M largest magnitude elements of the regression vector. We extend this approach from its existing form of MMax-NLMS to new afne projection and recursive least squares schemes with supporting analysis and simulation results. We discuss the computational complexity of these approaches for two alternative sort procedures. Finally, we extend the MMax criterion to a multichannel case by introducing an exclusivity constraint and show the effectiveness of the resulting XM tapselection criterion for application to stereophonic acoustic echo cancellation.

Robust variable step-size affine projection algorithm suitable for acoustic echo cancellation

The affine projection algorithm (APA) and different versions of it have proved to be very attractive choices for acoustic echo cancellation (AEC). In this context, a classical APA with a constant step-size has to compromise between two performance criteria, i.e., 1) high convergence rates and good tracking capabilities, and 2) low misadjustment and robustness against background noise variations and doubletalk. Consequently, a variable step-size APA (VSS-APA) is a more reliable solution. In this paper we propose a VSS-APA that is designed to recover the near-end signal from the error signal of the adaptive filter. Therefore, it is robust against near-end signal variations, including double-talk. Moreover, since it does not require a priori information about the acoustic environment, the proposed algorithm is easy to control in real-world AEC applications.

Family of affine projection adaptive filters with selective partial updates and selective regressors

IET Signal Processing, 2010

In this study the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection adaptive filtering algorithm are combined and the family of affine projection algorithms (APAs) with SPU and SR features are established. These algorithms are computationally efficient. The mean-square performance of the presented algorithms are analysed based on the energy conservation arguments of Sayed's group. This analysis does not need to assume a Gaussian or white distribution for the regressors. The authors demonstrate the performance of the presented algorithms through simulations. The good agreement between theoretically predicted and actually observed performances is also demonstrated. What we propose in this paper can be summarised as follows: † The establishment of the family of SPU-SR-APAs with low computational complexity and comparable convergence speed features. In these algorithms, the filter coefficients are partially updated and the input regressors are optimally selected. † A mean-square performance analysis of the family of SPU-SR-APAs. This analysis can also be applied to study the performance of the SPU and SR-APAs.

An Efficient Proportionate Affine Projection Algorithm for Echo Cancellation

IEEE Signal Processing Letters, 2000

Proportionate-type normalized least-mean-square algorithms were developed in the context of echo cancellation. In order to further increase the convergence rate and tracking, the "proportionate" idea was applied to the affine projection algorithm (APA) in a straightforward manner. The objective of this letter is twofold. First, a general framework for the derivation of proportionate-type APAs is proposed. Second, based on this approach, a new proportionate-type APA is developed, taking into account the "history" of the proportionate factors. The benefit is also twofold. Simulation results indicate that the proposed algorithm outperforms the classical one (achieving faster tracking and lower misadjustment). Besides, it also has a lower computational complexity due to a recursive implementation of the "proportionate history." Index Terms-Adaptive filtering, echo cancellation, proportionate affine projection algorithm.

Regularization of the Affine Projection Algorithm

IEEE Transactions on Circuits and Systems II: Express Briefs, 2000

The affine projection algorithm (APA) is an attractive choice for echo cancellation, mainly for its convergence features. A matrix inversion is required within the APA. For practical reasons, this matrix needs to be regularized, i.e., a positive constant is added to the elements of its main diagonal. This regularization parameter is of great importance in practice since, if it is not chosen properly, the APA may never converge, especially under low-signal-to-noise-ratio conditions. In this brief, we propose a formula for choosing the value of the regularization parameter, aiming at attenuating the effects of the noise in the adaptive filter estimate. Simulations performed in an acoustic echo cancellation scenario prove the validity of the approach in different noisy environments.

Optimal Regularization Parameter of the Multichannel Filtered-x Affine Projection Algorithm

IEEE Transactions on Signal Processing, 2007

We discuss the optimal regularization parameter of the Filtered-Affine Projection (FX-AP) algorithm suitable for feedforward active noise control. While the original FX-AP algorithm always provides a biased estimate of the minimum-meansquare solution, we show that the optimal regularized FX-AP algorithm is capable to eliminate the bias of the asymptotic solution and thus that the regularization parameter can optimize both the convergence speed and the residual MSE of the algorithm. We derive some expressions for the optimal regularization parameter, and we discuss some heuristic estimations of the optimal regularization parameter in practical conditions. Index Terms-Active noise control, affine projection algorithm, multichannel adaptive filtering, optimal regularization parameter.

IJERT-Application of Affine Projection Algorithm in Adaptive Noise Cancellation

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/application-of-affine-projection-algorithm-in-adaptive-noise-cancellation https://www.ijert.org/research/application-of-affine-projection-algorithm-in-adaptive-noise-cancellation-IJERTV3IS10644.pdf This paper presents the application of two classes of Affine Projection Algorithm (APA) for Adaptive Noise Cancellation. The output results are compared on the basis of signal to noise ratio (SNR) and frequency spectrum of the filtered signal. The two classes of Affine Projection Algorithm used to adapt the noise, involve Conventional APA and Adaptive Step Size APA. Computer Simulations for various classes of APA are carried out using Matlab. For colored input and correlated data, APA family is suitable to accelerate the convergence of Least Mean Squares (LMS) Algorithm at a computational cost. In adaptive step size APA, step size is adapted on the basis of absolute mean value of error vector.