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Adaptive filters are very useful in the present world. It is a well known fact that the performance of adaptive filters is highly dependent upon the type of input sequence. Further the uncorrelated input shows a better convergence speed than the correlated input. But most of the real world signals are correlated. Various methods have been proposed to preprocess these inputs by transforming these inputs...
This paper proposed a novel algorithm for combination of two adaptive filters which stem from different families. The algorithm extended the combination coefficient from a scale in traditional convex and affine combination methods to a vector. These improvements would significantly enhance the performances than any other existing methods under various interference environments, because the new method...
The transform domain normalized LMS (TD-NLMS)-adaptive filtering algorithm is an efficient adaptive filter with fast convergence speed and reasonably low arithmetic complexity. However, it is sensitive to the level of the excitation signal, which may vary significantly over time in speech and audio signals. This paper proposes a new regularized transform domain NLMS (R-TDNLMS) algorithm and studies...
In this paper we investigate a combination of two LMS adaptive algorithms, one with large and another one with small step size. Large step size of one of the filters allows fast initial convergence and small step size of the other filter allows a small steady state mean square error. The outputs of the two filters are combined together via a combination parameter λ. We compute this parameter using...
Recently, the augmented complex LMS (ACLMS) algorithm has been proposed for modeling complex-valued signal relationships in which a widely-linear model can be more appropriate. It is not clear, however, how the behavior of ACLMS differs from that of the conventional complex LMS (CCLMS) algorithm. In this paper, we leverage a recently-developed analysis for the complex LMS algorithm to illuminate the...
In order to improve the performance of LMS (Least Mean Square) adaptive filtering algorithm, an improved robustness adaptive step-size LMS equalization algorithm was presented by establishing a nonlinear relationship between the two relevant statistics for step-size factor μ(n) and the error signal e(n). Compared with other algorithms, this algorithm overcomes of sensitivity to the noise coming from...
Echo path estimation in echo canceling for teleconference system is a problem in double-talk condition. The correlation function based algorithms were defined by the authors to solve this problem. In this paper, in order to improve the convergence speed of correlation function based algorithm, we propose a new modified proportionate step-size adaptation method, and then implement it into frequency...
Improved versions of two known LMSN algorithms are proposed. In these algorithms, data-selective weight adaptation is performed and in this way reduced steady-state misalignment is achieved relative to that in the known LMSN algorithms while requiring a similar number of iterations to converge. On the other hand, for a constant misalignment a significant reduction in the convergence speed can be achieved...
Largely overlooked insights into the convergence behavior of the (N)LMS algorithm focusing on its worst and best case performance is presented. These insights motivate the use of the multigrid paradigm, well known from the numerical solution of partial differential equations, as an important tool in achieving improved convergence speed in (N)LMS-type adaptive filters. We present such a multigrid adaptive...
Adaptive filters based on the least-mean-square (LMS) algorithm in active noise control (ANC) system have an important limitation that the selection of a certain value for the step size implies compromise between speed of convergence and steady-state misadjustment. Traditional variable step size (VSS) LMS algorithms aiming at this problem have little efficiency in the high filter order case because...
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