A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this letter, a-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such a noise is a requirement of many practical problems. Since direct application of commonly used adaptation techniques fail in these applications, new algorithms for adaptive filtering for α-stable random processes are introduced. © 1994 IEE
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
The existence of impulsive signals with alpha-stable non-Gaussian distributions has already been rep...
We study the problem of filtering a Gaussian process whose trajectories, in some sense, have an unkn...
A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this pap...
Based on the concept of Fractional Lower Order Statistics (FLOS), we present the Robust Least Mean M...
Cataloged from PDF version of article.A new class of algorithms based on the fractional lower order ...
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impu...
Stochastic gradient-based adaptive algorithms are developed for the optimization of Weighted Myriad ...
DoctorAdaptive filters that self-adjust their transfer functions according to optimization algorithm...
The popular least-mean-squares (LMS) algorithm for adaptive filtering is nonrobust against impulsive...
In many real-life Bayesian estimation problems, it is appropriate to consider non-Gaussian noise dis...
This dissertation proposes four new algorithms based on fractionally lower order statistics for adap...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
In this paper, we consider the filtering of diffusion processes observed in non-Gaussian noise, when...
The aim of this paper is to introduce class of stable distributions as a potentional tool for statis...
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
The existence of impulsive signals with alpha-stable non-Gaussian distributions has already been rep...
We study the problem of filtering a Gaussian process whose trajectories, in some sense, have an unkn...
A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this pap...
Based on the concept of Fractional Lower Order Statistics (FLOS), we present the Robust Least Mean M...
Cataloged from PDF version of article.A new class of algorithms based on the fractional lower order ...
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impu...
Stochastic gradient-based adaptive algorithms are developed for the optimization of Weighted Myriad ...
DoctorAdaptive filters that self-adjust their transfer functions according to optimization algorithm...
The popular least-mean-squares (LMS) algorithm for adaptive filtering is nonrobust against impulsive...
In many real-life Bayesian estimation problems, it is appropriate to consider non-Gaussian noise dis...
This dissertation proposes four new algorithms based on fractionally lower order statistics for adap...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
In this paper, we consider the filtering of diffusion processes observed in non-Gaussian noise, when...
The aim of this paper is to introduce class of stable distributions as a potentional tool for statis...
This work is concerned with robustness, convergence, and stability of adaptive filtering (AF) type a...
The existence of impulsive signals with alpha-stable non-Gaussian distributions has already been rep...
We study the problem of filtering a Gaussian process whose trajectories, in some sense, have an unkn...