The minimum mean-square error (MMSE) and minimum error entropy (MEE) are two important criteria in the estimation related problems. The MMSE can be viewed as a robust MEE criterion in the minimax sense, as its minimization is equivalent to minimizing an upper bound (the maximum value) of the error entropy. This note gives a new and more meaningful interpretation on the robustness of MMSE for pr...

In many cases, one needs to estimate a target that is obscured by the sidelobes of a very large, adjacent scatterer. For example, the Earth's surface can ruin nearsurface rain-rate measurements obtained from an airborne platform, or it can prevent a ground-penetrating radar from detecting land mines located at shallow depth. In these cases, the range sidelobes of the ground are larger than the ...

The idea that common blind techniques based on Independent Component Analysis (ICA) behave in noisy environment like a biased MMSE separator (sometimes called Maximum Ratio Combiner (MRC)) was introduced in our recent work [3]. In this paper, we put this in more precise terms by doing an analysis of the bias of approaches that are based on known ICA algorithm FastICA. We show that the one-unit ...

This paper deals with the state estimation of dynamic systems. A recursive linear MMSE estimator is presented as an alternative to Kalman filtering . This estimator has the ability to cope with asynchronous measurements, and to process the data by sets of undefined sizes. It is particularly suitable for fault detection, because the decisions can be based on more data. This is an open door to ro...

In this paper, a channel estimator based on an adaptive neuro fuzzy inference system (ANFIS) is proposed for the purpose of estimating channel frequency responses in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems. To see the performance of our proposed channel estimation method, five different techniques including well-known pilot-based estima...

This work is motivated by the problem of estimation and classification of different electroencephalography (EEG) patterns elicited during various information processing tasks. Since the discovery of Hans Berger in 1929, interest in developing algorithms to process human brain and mind information by analyzing the EEG and ERP signals has been constantly growing. Both the EEG and ERP signals have...

We consider the Bayesian inference of a random Gaussian vector in a linear model with a Gaussian model matrix. We derive the maximum a-posteriori (MAP) estimator for this model and show that it can be found using a simple line search over a unimodal function that can be efficiently evaluated. Next, we discuss the application of this estimator in the context of nearoptimal detection of near-Gaus...

In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) estimator and a generalized minimum mean squared error (MMSE) estimator are developed. To overcome the drawback of signal distortions caused by down sampling of wavelet packet transform (WPT), a two-stage analytic decom...

In this paper, a residual echo cancellation method is proposed that uses an estimation of the minimum mean-square error (MMSE) based on a statistical model of a speech signal and an echo signal. After the suppression of the echo signal based on the adaptive filter, residual echo is further reduced by the proposed MMSE estimator and the results are compared with the conventional Wiener filter ba...

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficie...