Wide-band signal parameter estimation based on higher-order statistics

In this paper we develop an algorithm to improve the accuracy of the wideband signal parameter estimation. It is well known that in the presence of an unknown noise, these estimates may be grossly inaccurate. The proposed algorithm uses both the fourth order cumulant for the suppression of the gaussian noise, the transformation matrices for estimating the coherent cumulant matrix and a noneigenvector algorithm for the characterization of the sources. We show that the performances of bearing estimation algorithm improve substantially when the proposed algorithm is used. This method is tested on simulated data and its performances are clearly pointed out.

[1]  Munier.  1 - L'identification de fronts d'ondes corrélés et distordus , 1987 .

[2]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[3]  Claudine Latombe.  Amélioration du traitement d'antenne passif par l'estimation de la matrice spectrale des bruits Improvement of passive array treatment by estimation of the spectral matrix of the noises , 1987 .

[4]  Hong Wang,et al.  Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources , 1985, IEEE Trans. Acoust. Speech Signal Process..

[5]  Salah Bourennane,et al.  Optimal coherent subspace for bearing estimation , 1996, Proceedings of Digital Processing Applications (TENCON '96).

[6]  Thomas Kailath,et al.  Eigenstructure methods for direction of arrival estimation in the presence of unknown noise fields , 1986, IEEE Trans. Acoust. Speech Signal Process..

[7]  Mostafa Kaveh,et al.  Focussing matrices for coherent signal-subspace processing , 1988, IEEE Trans. Acoust. Speech Signal Process..

[8]  Salah Bourennane,et al.  New method for locating the wideband sources , 1996, 1996 IEEE Digital Signal Processing Workshop Proceedings.

[9]  Anthony J. Weiss,et al.  Direction finding using noise covariance modeling , 1995, IEEE Trans. Signal Process..

[10]  M. Frikel,et al.  Localization of the wideband sources with estimation of the antenna shape , 1996, Proceedings of 8th Workshop on Statistical Signal and Array Processing.

[11]  Jean-Pierre Le Cadre.  Parametric methods for spatial signal processing in the presence of unknown colored noise fields , 1989, IEEE Trans. Acoust. Speech Signal Process..

[12]  Salah Bourennane,et al.  Fast algorithm for the wideband array processing using a two-sided correlation transformation , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[13]  KUNGL TEKNISKA HÖGSKOLAN.  Direction Finding in the Presence of Spatially Correlated Noise Fields Direction Finding in the Presence of Spatially Correlated Noise Fields , 1994 .

[14]  Keith Q. T. Zhang,et al.  Information theoretic criteria for the determination of the number of signals in spatially correlated noise , 1993, IEEE Trans. Signal Process..

[15]  Ahmed H. Tewfik,et al.  Direction Finding In The Presence Of Colored Noise , 1989, Optics & Photonics.

[16]  Thomas Kailath,et al.  Direction of arrival estimation by eigenstructure methods with unknown sensor gain and phase , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Salah Bourennane,et al.  High-resolution methods without eigendecomposition for locating the acoustic sources , 1997 .

[18]  G. Bienvenu,et al.  Optimality of high resolution array processing using the eigensystem approach , 1983 .

[19]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.