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Transient behavior of affine projection algorithms
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004Most analytical results on affine projection algorithms assume special regression models or Gaussian regression data. The available analyses also treat different affine projection filters separately. This paper provides a unified treatment of the transient performance of a family of affine projection algorithms.
Shin, Hyun-Chool, Sayed, Ali H.
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Intermittently-updated affine projection algorithm
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013Acoustic echo cancellation and feedback cancellation systems require robust and computationally efficient adaptive filtering techniques. In this paper, a new affine projection algorithm with intermittent update of the filter coefficients is proposed where the update interval is determined according to the adaptation state.
Felix Albu +3 more
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Affine Projection Algorithm with Selective Regressors
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006Affine projection algorithm, which updates the weight vector based on several previous input vectors, is an useful adaptive filter to improve the convergence speed of LMS-type filter. However, the computational complexity of adaptation algorithm highly depends on the number of input vectors used for update.
Kyu-Young Hwang, Woo-Jin Song
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Convergence behavior of affine projection algorithms
IEEE Transactions on Signal Processing, 2000Summary: Over the last decade, a class of equivalent algorithms that accelerate the convergence of the normalized LMS (NLMS) algorithm, especially for colored inputs, has been discovered independently. The affine projection algorithm (APA) is the earliest and most popular algorithm in this class that inherits its name.
Sundar G. Sankaran, A. A. (Louis) Beex
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2015
The normalized least-mean-squares (NLMS) algorithm has a problem that the convergence slows down for correlated input signals. The reason for this phenomenon is explained by looking at the algorithm from a geometrical point of view. This observation motivates the affine projection algorithm (APA) as a natural generalization of the NLMS algorithm.
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The normalized least-mean-squares (NLMS) algorithm has a problem that the convergence slows down for correlated input signals. The reason for this phenomenon is explained by looking at the algorithm from a geometrical point of view. This observation motivates the affine projection algorithm (APA) as a natural generalization of the NLMS algorithm.
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A Stochastic Model for a Pseudo Affine Projection Algorithm
IEEE Transactions on Signal Processing, 2009This paper presents a statistical analysis of a Pseudo Affine Projection (PAP) algorithm, obtained from the Affine Projection algorithm (AP) for a step size alpha < 1 and a scalar error signal in the weight update. Deterministic recursive equations are derived for the mean weight and for the mean square error (MSE) for a large number of adaptive taps N
Sérgio J. M. de Almeida +2 more
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A Theory on the Convergence Behavior of the Affine Projection Algorithm
IEEE Transactions on Signal Processing, 2011In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA) based on the arguments of energy conservation. Although the APA and its convergence analysis have been widely studied, the dependency of weight-error vector on past noise is usually neglected for simplicity.
Kim, SE, Lee, JW, Song, WJ
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The data-selective constrained affine-projection algorithm
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001This paper introduces a constrained version of the recently proposed set-membership affine projection algorithm based on the set-membership criteria for coefficient update. The algorithm is suitable for linearly constrained minimum-variance filtering applications.
Stefan Werner 0001 +2 more
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Affine projection algorithms for sparse system identification
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose two versions of affine projection (AP) algorithms tailored for sparse system identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are based on the l1 norm, the proposed algorithms rely on homotopic l0 norm minimization, which has proven to yield better results in some practical contexts.
Markus V. S. Lima +2 more
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An Affine Projection Algorithm With Update-Interval Selection
IEEE Transactions on Signal Processing, 2013This paper presents a mean-square deviation (MSD) analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise
JaeWook Shin +3 more
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