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This paper studies the convergence analysis of the least mean M-estimate (LMM) and normalized least mean M-estimate (NLMM) algorithms with Gaussian inputs and additive Gaussian and contaminated Gaussian noises. These algorithms are based on the M-estimate cost function and employ error nonlinearity to achieve improved robustness in impulsive noise environment over their conventional LMS and NLMS counterparts...
This paper studies the convergence behaviors of the noise-constrained normalized least mean squares (NCNLMS) algorithm recently proposed in the work of Chan et al. (2008). Like its LMS counterpart, the NCNLMS algorithm employs the prior knowledge of the additive noise to adjust its step-size. Following (Wei et al., 2001), the convergence behaviors of the NCLMS under the noise mismatch cases are firstly...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the normalized least mean M-estimate (NLMM) algorithms. Our analysis is obtained by extending the framework of Bershad [6], [7], which were previously reported for the NLMS algorithm with Gaussian inputs. Due to the difficulties in evaluating certain expectations involved, in [6], [7] the behaviors of the NLMS...
This paper proposes a new sequential block partial update normalized least mean M-estimate (SB-NLMM) algorithm for adaptive filtering in impulsive noise environment. It utilizes the sequential partial update concept as in the sequential block partial update normalized least mean square (SB-NLMS) algorithm to reduce the computational complexity, while minimizing the M-estimate function for improved...
This paper proposes a new proportionate adaptive filtering algorithm which exploits the advantageous features of the generalized proportionate NLMS (GP-NLMS) algorithm and the fast LMS/Newton algorithm. By means of an efficient switching mechanism, the new algorithm works alternately between the GP-NLMS and the fast LMS/Newton algorithms in order to combine their respective advantages. The overall...
This paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not...
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