Identification of noncausal nonminimum phase AR models using higher-order statistics

Author

Tora, H. ; Wilkes, D.M.

Author_Institution

Dept. of Electr. & Comput. Eng., Vanderbilt Univ., Nashville, TN, USA

Volume

3

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1617

Abstract

In this paper, we address the problem of estimating the parameters of a noncausal autoregressive (AR) signal from estimates of the higher-order cumulants of noisy observations. The proposed family of techniques uses both 3rd-order and 4th order cumulants of the observed output data. Consequently, at low SNR, they provide superior performance to methods based on autocorrelations. The measurement noise is assumed to be Gaussian and may be colored. The AR model parameters here are directly related to the solution of a generalized eigenproblem. The performance is illustrated by means of simulation examples

Keywords

Gaussian noise; autoregressive processes; eigenvalues and eigenfunctions; higher order statistics; parameter estimation; signal processing; 3rd order cumulants; 4th order cumulants; AR model parameters; Gaussian noise; colored noise; generalized eigenproblem; higher-order cumulants; higher-order statistics; identification; low SNR; noisy observations; noncausal autoregressive signal; noncausal nonminimum phase AR models; parameter estimation; Additive noise; Autocorrelation; Gaussian noise; Gaussian processes; Higher order statistics; Parameter estimation; Phase estimation; Phase noise; Signal processing; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.756299

Filename

756299