Article

Partial-Update L∞ -Norm Based Algorithms

Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que.
Circuits and Systems I: Regular Papers, IEEE Transactions on (impact factor: 1.97). 03/2007; DOI:10.1109/TCSI.2006.883863
Source: IEEE Xplore

ABSTRACT The computational complexity of an adaptive filtering algorithm increases with increasing the filter tap length and therefore, the use of such a filter can become prohibitive for certain applications, especially for real-time implementation. In this paper, we develop low-complexity adaptive filtering algorithms by incorporating the concept of partial updating of the filter coefficients into the technique of finding the gradient vector in the hyperplane based on the Linfin-norm criterion. Two specific partial update algorithms based on the sequential and M-Max coefficient updating are proposed. The statistical analyses of the two algorithms are carried out, and evolution equations for the mean and mean-square of the filter coefficient misalignment as well as the stability bounds on the step size are obtained. It is shown that the proposed partial update algorithm employing the M-Max coefficient updating can achieve a convergence rate that is closest to that of the full update algorithm. Finally, simulations are carried out to validate the theoretical results and study the convergence rate of the proposed algorithms

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    Article: A Subband Adaptive Filtering Algorithm Employing Dynamic Selection of Subband Filters
    Seong-Eun Kim, Young-Seok Choi, Moon-Kyu Song, Woo-Jin Song
    [show abstract] [hide abstract]
    ABSTRACT: We present a novel normalized subband adaptive filter (NSAF) which dynamically selects subband filters in order to reduce computational complexity while maintaining convergence performance of conventional NSAF. The selection operation is performed to achieve the largest decrease between the successive mean square deviations at every iteration. As a result, an efficient and competent NSAF algorithm is derived. The experimental results show that the proposed NSAF algorithm gains an advantage over the conventional NSAF in that it leads to a similar convergence performance with a substantial saving of overall computational burden.
    IEEE Signal Processing Letters 04/2010; · 1.39 Impact Factor
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Keywords

algorithm
 
algorithm increases
 
algorithms
 
computational complexity
 
convergence rate
 
filter coefficients
 
filter tap length
 
gradient vector
 
incorporating
 
L<sub>infin</sub>-norm criterion
 
M-Max coefficient updating
 
partial updating
 
proposed algorithms
 
proposed partial
 
real-time implementation
 
stability bounds
 
statistical analyses
 
step size
 
theoretical results
 
two algorithms