Prediction error method for second-order blind identification

Blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest. In this paper, we introduce a second-order blind identification technique based on a linear prediction approach. In contrast to eigenstructure-based methods, it will be shown that the linear prediction error method is "robust" to order overdetermination. An asymptotic performance analysis of the proposed estimation method is carried out, consistency and asymptotic normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are finally presented to demonstrate the potential and the "robustness" of the proposed method.

[1]  Benjamin Friedlander,et al.  On the computation of the Cramer Rao bound for ARMA parameter estimation , 1984, ICASSP.

[2]  Sophia Antipolis Cedex,et al.  BLIND FRACTIONALLY-SPACED EQUALIZATION, PERFECT-RECONSTRUCTION FILTER BANKS AND MULTICHANNEL LINEAR PREDICTION , 1994 .

[3]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Sumit Roy,et al.  A new blind time-domain channel identification method based on cyclostationarity , 1994, IEEE Signal Processing Letters.

[5]  William A. Gardner,et al.  A new method of channel identification , 1991, IEEE Trans. Commun..

[6]  Hui Liu,et al.  A deterministic approach to blind symbol estimation , 1994 .

[7]  Ehud Weinstein,et al.  New criteria for blind deconvolution of nonminimum phase systems (channels) , 1990, IEEE Trans. Inf. Theory.

[8]  Lang Tong,et al.  A new approach to blind identification and equalization of multipath channels , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[9]  B. Anderson,et al.  Greatest common divisor via generalized Sylvester and Bezout matrices , 1978 .

[10]  Lang Tong,et al.  Blind identification and equalization based on second-order statistics: a time domain approach , 1994, IEEE Trans. Inf. Theory.

[11]  Benjamin Friedlander On the computation of the Cramer-Rao bound for ARMA parameter estimation , 1984 .

[12]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[13]  E. J. Hannan,et al.  Multiple time series , 1970 .

[14]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[15]  Hui Liu,et al.  A deterministic approach to blind symbol estimation , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[16]  Philippe Loubaton,et al.  Prediction error methods for time-domain blind identification of multichannel FIR filters , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[17]  Tosio Kato Perturbation theory for linear operators , 1966 .

[18]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[19]  R. Serfling Approximation Theorems of Mathematical Statistics , 1980 .

[20]  Thomas Kailath,et al.  Linear Systems , 1980 .

[21]  John G. Proakis,et al.  Digital Communications , 1983 .