On eigenvalue decomposition estimators of centro-symmetric covariance matrices

Signal Processing

Volumn 78, Issue 1, 1999, Pages 101-116

  Delmas, Jean Pierre ^a  

^a INSTITUT NATIONAL DES TÉLÉCOMMUNICATIONS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; EIGENVALUES AND EIGENFUNCTIONS; MATRIX ALGEBRA;

CENTRO-SYMMETRIC COVARIANCE MATRIX; EIGENVALUE DECOMPOSITION (EVD) ESTIMATORS;

SIGNAL FILTERING AND PREDICTION;

EID: 0032646567     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1684(99)00050-X     Document Type: Article

References (22)

1. T.W. Anderson, An Introduction to Multivariate Statistical Analysis, Second Edition, Wiley and Sons, 1984.
2. A. Benveniste, M. Métivier, P. Priouret, Adaptive Algorithms and Stochastic Approximations, Springer, Verlag, 1990.
3. D.R. Brillinger, Times Series, Data Analysis and Theory, Expanded Edition, Holden-Day, Inc., 1980.
4. Bunse-Gerstner A., Byers R., Mehrmann V. A chart of numerical methods for structured eigenvalue problems. SIAM J. Matrix Anal. Appl. 13(2):April 1992;419-453.
5. Cantoni A., Butler P. Eigenvalues and eigenvectors of symmetric centrosymmetric matrices. Linear Algebra and its Applications. 13:1976;275-288.
6. Datta L., Morgera S.D. On the reducibility of centrosymmetric matrices: Applications in engineering problems. Circuits Systems Signal Process. 8(1):1989;71-96.
7. Delfosse N., Loubaton P. Adaptive blind separation of independent sources: A deflation approach. Signal Processing. 45:1995;59-83.
8. J.P. Delmas, Performance analysis of parametrized adaptive eigensubspace algorithms, in: Proc. ICASSP Detroit, May 1995, pp. 2056-2059.
9. Delmas J.-P. Performance analysis of a Givens parametrized adaptive eigenspace algorithms. Signal Processing. 68:1998;87-105.
10. Delmas J.P., Alberge F. Asymptotic performance analysis of subspace adaptive algorithms introduced in the neural network literature. IEEE Trans. Signal Process. 46(1):January 1988;170-182.
11. P. Delsarte, Y, Genin, Spectral properties of finite Toeplitz matrices, in: Proc. Internat. Symp. on the Math. Theory of Networks and Systems, Vol. 58, Springer, New York, 1984, pp. 194-213.
12. G.H. Golub, C.F. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, MD, 1990.
13. Hornik K., Kuan C.M. Convergence analysis of local feature extraction algorithms. Neural Networks. 5:1992;229-240.
14. Kaveh M., Barabell A.J. The statistical performance of the MUSIC and the Minimum-Norm algorithms in resolving plane waves in noise. IEEE Trans. ASSP. 34(2):April 1986;331-341.
15. Moulines E., Duhamel P., Cardoso J.F., Mayrargue S. Subspace methods for blind identification of multichannel FIR filters. IEEE Trans. on Signal Process. 43(2):February 1995;516-525.
16. Oja E. A simplified neuron model as a principal components analyzer. Journal of Math. Biology. 15:1982;267-273.
17. J. Oja, Subspace Methods of Pattern Recognition, Research Studies Press and John Wiley and Sons, Letchworth, England, 1983.
18. Oja E., Karhunen J. On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix. Journal of Math. Analysis and Applications. 106:1985;69-84.
19. F. Vanpoucke, S.H. Jensen, M. Moonen, A Persymmetric adaptive eigenfilter-bank for real data, in: Proc. of Asilomar-29, October 1995, pp. 1342-1346.
20. G. Xu, Y. Cho, T. Kailath, Application of fast subspace decomposition to signal processing and communication problems, IEEE Trans. on Signal Process. 42 (6) (June 1994) 1543-1461.
21. Xu G., Roy R.H., Kailath T. Detection of number of sources via exploitation of centro-symmetry property. IEEE Trans. Signal Process. 42(1):January 1994;102-112.
22. B. Yang, F. Gersemsky, Asymptotic distribution of recursive subspace estimators, in: Proc. ICASSP Atlanta, May 1996, pp. 1764-1767.