Total Least Squares for Affinely Structured Matrices and the Noisy Realization Problem

IEEE Transactions on Signal Processing

Volumn 42, Issue 11, 1994, Pages 3104-3113

  de Moor, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONTROL THEORY; CONVERGENCE OF NUMERICAL METHODS; DIGITAL SIGNAL PROCESSING; IDENTIFICATION (CONTROL SYSTEMS); ITERATIVE METHODS; MATRIX ALGEBRA; OPTIMIZATION; SIGNAL THEORY; STATISTICAL METHODS; TIME VARYING SYSTEMS;

AFFINELY STRUCTURED MATRICES; CADZOW ITERATION; FINITE IMPULSE RESPONSE SYSTEM; LINEAR TIME INVARIANT; MAXIMUM LIKELIHOOD ITERATION; NOISY REALIZATION PROBLEM; SINGULAR VALUE DECOMPOSITION; STEIGLITZ MCBRIDE ALGORITHMS; TOEPLITZ MATRIX APPROXIMATIONS;

LEAST SQUARES APPROXIMATIONS;

EID: 0028542789     PISSN: 1053587X     EISSN: 19410476     Source Type: Journal    
DOI: 10.1109/78.330370     Document Type: Article

References (11)

1. T. J. Abatzoglou, J. M. Mendel, and G. A. Harada, “The constrained total least squares technique and its application to harmonic superresolution,” IEEE Trans. Signal Processing, vol. 39, no. 5, pp. 1070–1087, 1991.
2. Y. Bresler and A. Macovski, “Exact maximum likelihood parameter estimation of superimposed exponential signals in noise,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, no. 5, pp. 1081–1089, Oct. 1986.
3. J. Cadzow, “Signal enhancement: a composite property mapping algorithm,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 49–62, 1988.
4. M. P. Clark and L. L. Scharf, “On the complexity of IQML algorithms,” IEEE Trans. Signal Processing, vol. 40, no. 7, pp. 1811–1813, 1992.
5. B. De Moor and G. H. Golub, “The restricted singular value decomposition: Properties and applications,” SIAM J. Matrix Anal., Applicat., vol. 12, no. 3, pp. 401–425, 1991.
6. B. De Moor, “Structured total least squares and L2 approximation problems,” Linear Algebra and its Applications, Special Issue on Numerical Linear Algebra Methods in Control, Signals and Systems (Van Dooren, Ammar, Nichols, Mehrmann, Eds.), vol. 188–189, pp. 163–207, 1993.
7. B. De Moor, P. Van Overschee and G. Schelfhout, “H2-model reduction for SISO systems,” in Proc. 12th World Cong. Int. Fed. Automat. Contr. (Sydney, Australia), July 18-23, 1993, vol. II, pp. 227–230.
8. B. Hanzon, “The area enclosed by the oriented Nyquist diagram and the Hilbert-Schmidt-Hankel norm of a linear system,” IEEE Trans. Automat. Contr., vol. 37, no. 6, June 1992.
9. R. Kumaresan, L. Scharf, and A. Shaw, “An algorithm for pole-zero modeling and spectral estimation,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 637–640, June 1986.
10. J. H. McClellan and D. Lee, “Exact equivalence of the Steiglitz-McBride iteration and IQML,” IEEE Trans. Signal Processing, vol. 39, pp. 509–512, Feb. 1991.
11. K. Steiglitz and L. E. McBride, “A technique for identification of linear systems,” IEEE Trans. Automat. Cont., vol. AC-10, pp. 461–464, 1965.