Despite their major advantage of hardware simplicity, ternary filters have limited usability in practice owing to their unresolved problem of adaptivity. This challenging problem is tackled by introducing an adaptive ternary LMS-like algorithm. Performance assessment using a sinusoidal input distorted by additive white Gaussian noise showed that the proposed algorithm is comparable to the tradi...

The two-dimensional least mean square ( 2 0 LMS) adaptive filters have been recently used in the image processing applications for reducing the noise. I n this paper, a new two-dimensional LMS algorithm is proposed. For the special desires to the linear phase constraint during filtering the images, a n additional linear phase constraint is added to the existing 2 0 LMS algorithms. Compare with ...

The performance of gradient search adaptive filters, such as the least mean squares (LMS) algorithm, may degrade badly when the filter is subjected to input signals which are corrupted by impulsive interference. The median LMS (MLMS) adaptive filter is designed to alleviate this problem by protecting the filter coefficients from the impact of the impulses. MLMS is a modification of LMS, obtaine...

Widrow proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm. The paper gives the statistical analysis for this ...

This paper proposes a novel scheme of adaptive system identification for acoustics enhancement applications. A key contribution of this study is the development of the variable exponentially-weightedstepsize LMS (VES) algorithm based on the statistics of the overall varying characteristics of the estimate of room impulse responses. The modelling error of the VES algorithm can reduce to -20dB af...

This paper proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The propos...

In this paper, a transform domain normalised partially decoupled LMS (TP-LMS) algorithm is proposed based on the partially decoupled transform domain Volterra filter. It is formulated by applying an orthogonal transform to the first order of the partially decoupled Volterra filter, in which the filter weights of a given order are optimised independently of those in the higher order. This approa...

The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the ad...