Fast approximate inverse power iteration algorithm for adaptive total least-squares FIR filtering

IEEE Transactions on Signal Processing

Volumn 54, Issue 10, 2006, Pages 4032-4039

  Feng, Da Zheng ^a   Zheng, Wei Xing ^b  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF WESTERN SYDNEY   (Australia)

Author keywords

Approximate inverse power iteration; Fast gain vector; Finite impulse response (FIR) filtering; Galerkin method; Global convergence; Total least squares (TLS)

Indexed keywords

ADAPTIVE FILTERING; COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; FIR FILTERS; GALERKIN METHODS; LEAST SQUARES APPROXIMATIONS;

APPROXIMATE INVERSE POWER ITERATION; GLOBAL CONVERGENCE; TOTAL LEAST SQUARES (TLS);

SIGNAL PROCESSING;

EID: 33749374402     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2006.880245     Document Type: Article

References (18)

1. S. Haykin, Adaptive Filter Theory, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
2. G. H. Golub and C. F. van Loan, Matrix Computations. Baltimore, MD: The Johns Hopkins Univ. Press, 1983.
3. R. Vaccaro, "On adaptive implementations of Pisarenko's harmonic retrieval method," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), San Diego, CA, Mar. 1984, vol. 9, pp. 224-227.
4. H. Chen, T. K. Sarkar, S. A. Dianat, and J. D. Brule, "Adaptive spectral estimation by the conjugate gradient method," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-34, no. 2, pp. 272-284, Apr. 1986.
5. V. U. Reddy, B. Egardt, and T. Kailath, "Least squares-type algorithm for adaptive implementations of Pisarenko's harmonic retrieval method," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-34, no. 3, pp. 399-405, Jun. 1982.
6. C. E. Davila, "An efficient recursive total least squares algorithm for FIR adaptive filtering," IEEE Trans. Signal Process., vol. 42, no. 2, pp. 268-280, Feb. 1994.
7. L. Ljung, M. Morf, and D. Falconer, "Fast calculation of gain matrices for recursive estimation schemes," Int. J. Control, vol. 27, no. 1, pp. 1-19, 1978.
8. S. Ljung and L. Ljung, "Error propagation properties of recursive least-squares adaptation algorithms," Automatica, vol. 21, no. 2, pp. 157-167, Mar. 1985.
9. J. L. Botto and G. V. Moustakides, "Stabilization of fast Kalman algorithms," IEEE Trans. Acoust., Speech Signal Process., vol. ASSP-37, no. 9, pp. 1342-1348, Sep. 1989.
10. D. T. Slock and T. Kailath, "Numerically stable fast transversal filters for recursive least squares adaptive filtering," IEEE Trans. Signal Process., vol. 39, no. 1, pp. 92-114, Jan. 1991.
11. C. E. Davila, "Line search algorithms for adaptive filtering," IEEE Trans. Signal Process., vol. 4 1, no. 7, pp. 2490-2494, Jul. 1994.
12. D.-Z. Feng, X.-D. Zhang, D.-X. Chang, and W. X. Zheng, "A fast recursive total least squares algorithm for adaptive FIR filtering," IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2729-2737, Oct. 2004.
13. B. E. Dunne and G. A. Williamson, "QR-basedfLS and mixed LS-TLS algorithms with applications to adaptive IIR filtering," IEEE Trans. Signal Process., vol. 51, no. 2, pp. 386-394, Feb. 2003.
14. B. E. Dunne and G. A. Williamson, "Analysis of gradient algorithms for TLS-based adaptive IIR filters," IEEE Trans. Signal Process., vol. 52, no. 12, pp. 3345-3356, Dec. 2004.
15. A. Yeredor, "The extended least squares criterion: Minimization algorithms and applications," IEEE Trans. Signal Process., vol. 49, no. 1, pp. 74-86, Jan. 2001.
16. Y. N. Rao, D. Erdogmus, and J. C. Principe, "Error whitening criterion for adaptive filtering: Theory and algorithms," IEEE Trans. Signal Process., vol. 53, no. 3, pp. 1057-1069, Mar. 2005.
17. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc. Nat. Acad. Sci. USA, vol. 79, pp. 2554-2558, 1982.
18. K. H. Huebner and E. A. Thornton, The Finite Element Method for Engineers. New York: Wiley. 1982.