We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed al...

In this paper, normalized least mean (NLMS) square and recursive least squares (RLS) adaptive channel estimator are described for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. These CE methods uses adaptive estimator which are able to update parameters of the estimator continuously, so that the knowledge of channel and noise statistics are not ...

The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear FIR ltering. It provides an automatic choice for the LMS step-size parameter which aaects the stability, convergence speed and steady-state performance of the algorithm. In this paper, we generalize the NLMS algorithm by deriving a class of Nonlinear Normalized LMS-type (...

Two-dimensional (TD) adaptive filtering is a technique that can be applied to many image, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to TD structures and the novel TD adaptive filters are established. Based on this extension, the TD variable step-size normalized least mean squares (TD-VSS-NLMS), the TD-VSS affine projection algorithms (...

The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear ltering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS step-size parameter which aaects the stability , steady-state mean square error (MSE) and convergence speed of...

The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed o...

Abstract Non-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, denoising accuracy of NLMs decreases significantly. This paper further develop NLMs-based method remove with less loss details. It is realized by embedding an optimal graph edge weights driven kernel into a...

In this paper, a technique to identify the filter bank coefficients of Wavelets db4 and coif5 using adaptive filter NLMS algorithm is presented. Filter bank coefficients of the wavelet are treated as the weight vector of adaptive filter, changes with each iteration and approach to the desired value after little iteration. When we compare the two adaptive algorithms viz. Least Mean Square (LMS) ...

To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model due to broadband signal transmission, such sparsity can be exploited by adaptive sparse channel estimation (ASCE) methods using sparse ISS-NLMS algorit...

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It...