HomeChair: Jitendra K. Tugnait, Auburn University, USA
Home
Yujiro Inouye, Shimane University (Japan)
Kazuaki Tanebe, Osaka University (Japan)
Multichannel blind deconvolution has received increasing attention during the last decade. Recently, Martone (3, 4) extended the super-exponential method proposed by Shalvi and Weinstein (1, 2) for single-channel blind deconvolution to multichannel blind deconvolution. However, the Martone extension suffers from two types of serious drawbacks. The objective of this paper is to obviate these drawbacks and to propose three approaches to multichannel blind deconvolution. In the first approach, we present a multichannel super-exponential algorithm. In the second approach, we present a super-exponential deflation algorithm. In the third approach, we present a two-stage super-exponential algorithm.
ic981372.pdf (From Postscript)
TOP
Yifeng Zhou, Telexis Corporation (Canada)
Henry Leung, SurfaceRadar Section, DREO (Canada)
Patrick C. Yip, McMaster University (Canada)
In this paper, an efficient blind identification algorithm for multichannel FIR systems was proposed based on a deterministic model of the channel input. By decoupling the multichannel identification, the proposed method was able to estimate each individual channel responses separately without having to solve for the augmented channel responses. The algorithm was implemented using linear prediction techniques. It was computationally efficient and suitable for real-time applications. Computer simulations were used to demonstrate the effectiveness of the proposed algorithm.
ic981511.pdf (From Postscript) TOP
Santiago Zazo, Universidad Alfonso X El Sabio (Spain)
Jose Manuel Paez-Borrallo, Universidad Politecnica de Madrid (Spain)
It is well known that blind channel deconvolution enables the receiver to equalize the channel simply by analyzing the received digital signal. Much of the work in 1990's faces the challenge presented by multiple-output systems, exploiting cyclostationarity properties and multivariate formulation of the incoming data. Our proposal is twofold: on one hand, we develop a theoretical analysis of a new blind channel deconvolution scheme by the exploitation of some shifting properties of the autocorrelation matrices of the source, in order to propose an appropriate cost function; on the other hand, an efficient programming is considered based on a Generalized Rayleigh Quotient formulation by using a Conjugate Gradient algorithm.
ic981545.pdf (From Postscript) TOP
Andrew E. Yagle, University of Michigan (U.S.A.)
Rajashri R Joshi, University of Michigan (U.S.A.)
We consider the problem of computing the maximum likelihood estimates of the reflection coefficients of adiscrete 1-D layered medium from noisy observations of its impulse reflection response. We have side information in that a known subset of the reflection coefficients are known to be zero; this knowledge could come from either a priori knowledge of a homogeneous subregion inside the scattering medium, or from a thresholding operation in which noisy reconstructed reflection coefficients with absolute values below a threshold are known to be zero. Our approach is simple, noniterative, and requires only solutions of systems of linear equations. Numerical examples are provided which demonstrate not only the operation of the algorithm, but also that the side information improves the reconstruction of unconstrained reflection coefficients as well as constrained ones, due to the nonlinarity of the problem.
ic981861.pdf (From Postscript) TOP
Jonathan H Manton, The University of Melbourne (Australia)
Yingbo Hua, The University of Melbourne (Australia)
Yufan Zheng, The University of Melbourne (Australia)
Cishen Zhang, The University of Melbourne (Australia)
It is a standard result that a finite impulse response channel of length L can be uniquely identified by feeding in a known (and persistently exciting) sequence of 2L-1 consecutive data points. Equivalently, given only 2L-2 consecutive data points, the channel can be uniquely identified up to a multiplicative constant. This paper significantly extends the identifiability criterion to the case when the known inputs are non-consecutively located. It is argued that by introducing 2L-1 non-consecutively spaced zeros into the input stream, for almost all input sequences, the channel can be uniquely identified up to a multiplicative constant. Furthermore, the result can be extended to the case when the known inputs are non-zero, in which case the channel can almost always be identified uniquely. To arrive at these results, general properties of systems of polynomial equations are derived. These properties do not seem to have appeared in the literature before. Key words: Algebraic geometry, Commutative algebra, Polynomial equations, Semi-blind identification, Finite impulse response channels.
ic981865.pdf (From Postscript) TOP
Vikram K. Krishnamurthy, University of Melbourne (Australia)
Leigh A Johnston, University of Melbourne (Australia)
Andrew Logothetis, University of Melbourne (Australia)
In this paper we present a finite dimensional iterative algorithm for optimal maximum a posteriori (MAP) state estimation of bilinear systems. Bilinear models are appealing in their ability to represent or approximate a broad class of nonlinear systems. We show that several bilinear models previously considered in the literature are special cases of the general bilinear model we propose. Our iterative algorithm for state estimation is based on the Expectation-Maximization (EM) algorithm and outperforms the widely used Extended Kalman filter (EKF). Unlike the EKF, our algorithm is an optimal (in the MAP sense) finite-dimensional solution to the state sequence estimation problem for bilinear models.
ic981887.pdf (From Postscript) TOP
Pierre Fabry, INPG-LIS (France)
Christine Serviere, INPG-LIS (France)
Jean-Louis Lacoume, INPG-LIS (France)
In this paper, we adress the issue of Orthogonal Techniques for Blind Source Separation of periodic signals when the mixtures are corrupted with spatially correlated noises.The noise covariance matrix is assumed to be unknown. This problem is of major interest with experimental signals. We first remind that the Principal Component Analysis (PCA) cannot provide a correct estimate of the signal subspace when the noises are spatially correlated or when their power spectral densities are different. We then introduce a new estimator of the unnoisy spectral matrix using delayed blocks. The only assumption is that the noise correlation and cross-correlation lenghs must be shorter than the source correlation lenghs. Simulation results show the efficiency of the new method.
ic982069.pdf (From Postscript) TOP
Yih-Choung Yu, University of Pittsburgh (U.S.A.)
Marwan Simaan, University of Pittsburgh (U.S.A.)
J. Robert Boston, University of Pittsburgh (U.S.A.)
Philip J. Miller, Novacor, Baxter Healthcare (U.S.A.)
James F. Antaki, University of Pittsburgh (U.S.A.)
This paper describes the use of signal processing techniques to estimate the model parameters of a leftventricular assist device (LVAD). The model consisted of lumped resistance, capacitance, and inductance elements with one time-varying capacitor to estimate the cyclical pressure generation of the device using volume signal from the device. The model parameters were estimated by least squares fit to experimental data obtained in the laboratory. The purpose of this research is to estimate the pressure and flow signals, which are usually measured through invasive physiologic sensors, for an on-line estimator to identify cardiovascular parameters of patients who are under LVAD assist. The success of this development would provide a useful tool to monitor the cardiac function of LVAD patients without indwelling sensors. A computer simulation of the pump with a cardiovascular model was developed to demonstrate the interaction between the LVAD and the cardiovascular system. The simulation results showed agreement with those from an animal experiment and thus the simulation waveforms can be used for testing the cardiovascular estimator.
ic982189.pdf (From Postscript) TOP
Azzedine Touzni, ETIS (France)
Inbar Fijalkow, ETIS (France)
Micheal G. Larimore, Applied Signal Technology (U.S.A.)
John R. Treichler, Applied Signal Technology (U.S.A.)
We address the deconvolution of MIMO linear mixtures. The approach is based on the construction of a hierarchical familly of composite criteria involving CM criterion and second order statistics constraint.Altougth, the criteria are based on fourth order statistics, we give a complet proof of convergence of this structure. We show that each cost function leads to the restoration of one single source. Moreover the approach is naturally robust with respect to the channels order estimation. An adaptive alorithm is derived for the simultaneous estimation of all sources.
ic982464.pdf (From Postscript) TOP
Gianpiero Panci, Dip. INFOCOM, Universita di Roma (Italy)
Gaetano Scarano, Dip. INFOCOM, Universita di Roma (Italy)
Giovanni Jacovitti, Dip. INFOCOM, Universita di Roma (Italy)
In this contribution we address the problem of the blind joint identification and order estimation of a non-minimum phase FIR communication channel by exploiting the cyclostationarity of the received signal sampled at rate greater of the symbol rate. We show that the identification can be formulated as a ""subspace fitting"" problem; this allows for using the subspace distance as a test statistic to detect the correct channel length (among different hyphoteses). Moreover, mimicking estimation procedures proposed in the framework of DOA estimation, an asymptotically efficient procedure is proposed which obtains the channel estimate in two steps: the channel estimate obtained in the first step determines optimal weights which are then employed in the second step to refine the channel estimate. The accuracy of the identification is comparable with other methods described in the literature (e.g. the subspace method [4]) while the test for order detection performs quite well also in the presence of channel disparity outperforming commonly used tests based on the eigenvalues of the covariance matrix.
ic985188.pdf (Scanned) TOP