Blind System Identification Using Symbolic Dynamics

Blind system identification using minimum noise subspace

Developing fast and robust methods for identifying multiple FIR channels driven by an unknown common source is important for wireless communications, speech reverberation cancellation, and other applications. In this correspondence, we present a new method that exploits a minimum noise subspace (MNS). The MNS is computed from a set of channel output pairs that form a “tree.” The “tree” exploits...

Blind system identification using kernel-based methods

We propose a new method for blind system identification (BSI). Resorting to a Gaussian regression framework, we model the impulse response of the unknown linear system as a realization of a Gaussian process. The structure of the covariance matrix (or kernel) of such a process is given by the stable spline kernel, which has been recently introduced for system identification purposes and depends ...

Blind System Identification

A New Approach to Detect Congestive Heart Failure Using Symbolic Dynamics Analysis of Electrocardiogram Signal

The aim of this study is to show that the measures derived from Electrocardiogram (ECG) signals many a time perform better than the same measures obtained from heart rate (HR) signals. A comparison was made to investigate how far the nonlinear symbolic dynamics approach helps to characterize the nonlinear properties of ECG signals and HR signals, and thereby discriminate between normal and cong...

Multiple Antenna System Equalization Using Semi-blind Subspace Identification Methods

In this paper, we investigate the application of the subspace system identification (SSI) method (e.g. N4SID) to the MIMO frequency-selective fading channel estimation problem. The FIR constraint on the MIMO channel model is suggested to be relieved to draw benefit from possible parsimonious parametrization of the MIMO channel when subchannels become correlated. Also, the criterion for training...