Estimation of signals in coloured non-Gaussian noise based on Gaussian Mixture Models

Pradeepa, R. ; Anand, G. V. (2006) Estimation of signals in coloured non-Gaussian noise based on Gaussian Mixture Models Nonlinear Statistical Signal Processing Workshop, 13-15 Sept . pp. 17-20.

Full text not available from this repository.

Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/NSSPW.2006.4378810

Abstract

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.

Item Type:Article
Source:Copyright of this article belongs to IEEE.
ID Code:89752
Deposited On:30 Apr 2012 14:53
Last Modified:30 Apr 2012 14:53

Repository Staff Only: item control page