A Method for Recursive Least Squares Filtering Based Upon an Inverse Qr Decomposition

IEEE Transactions on Signal Processing

Volumn 41, Issue 1, 1993, Pages 20-30

  Alexander, S Thomas ^a   Ghirnikar, Avinash L ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INVERSE QR DECOMPOSITION; LEAST SQUARES FILTERING; QR DECOMPOSITION; RECURSIVE FILTERING;

ALGORITHMS; ELECTRIC FILTERS; LEAST SQUARES APPROXIMATIONS; SYSTEM STABILITY;

SIGNAL FILTERING AND PREDICTION;

EID: 0027149349     PISSN: 1053587X     EISSN: 19410476     Source Type: Journal    
DOI: 10.1109/TSP.1993.193124     Document Type: Article

References (21)

1. S. Haykin, Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice-Hall, 1986.
2. S. T. Alexander, Adaptive Signal Processing. New York: Springer-Verlag, 1986.
3. G. Carayannis, D. G. Manolakis, and N. Kalouptsidis, “A fast sequential algorithm for least-squares filtering and prediction,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-31, pp. 1394–1402, Dec. 1983.
4. D. D. Falconer and L. Ljung, “Application of fast Kalman estimation to adaptive equalization,” IEEE Trans. Commun., vol. COM-26, pp. 1439–1446, Oct. 1978.
5. J. M. Cioffi and T. Kailath, “Fast RLS transversal filters for adaptive filtering,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 304–337, June 1984.
6. J. M. Cioffi, “Limited precision effects for adaptive filtering,” IEEE Trans. Circuits Syst., vol. CAS-34, 821–833, July 1987.
7. P. Fabre and C. Gueguen, “Improvement of fast recursive least squares algorithms via normalization: a comparative study,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 296–308, Apr. 1986.
8. J. L. Botto and G. V. Moustakides, “Stabilizing the fast Kalman algorithms,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, 1342–1348, 1989.
9. D. T. M. Slock and T. Kailath, “Numerically stable fast transversal filters for recursive least squares adaptive filtering,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 39, pp. 92–114, Jan. 1991.
10. J. M. Cioffi, “The fast adaptive ROTOR's RLS algorithm,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 631–653, Apr. 1990.
11. P. A. Regalia and M. G. Bellanger, “On the duality between fast QR methods and lattice methods in least squares adaptive filtering,” IEEE Trans. Signal Processing, vol. 39, pp. 879–891, Apr. 1991.
12. H. Leung and S. Haykin, “Stability of recursive QRD-LS algorithms using finite precision systolic array implementation.” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 760–763, May 1989.
13. C. T. Pan and R. J. Plemmons, “Least squares modifications with inverse factorizations: parallel implications,” J. Comput., Appl. Math., vol. 27, pp. 109–127, 1989.
14. C. M. Rader and A. O. Steinhardt, “Hyperbolic Householder transformations,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 1589–1602, Dec. 1986.
15. G. H. Golub and C. Van Loan, Matrix Computations. Baltimore: Johns Hopkins Press, 1983.
16. W. M. Gentleman and H. T. Kung, “Matrix triangularization by systolic arrays,” Proc. SPIE, Int. Soc. Opt. Eng., vol. 298, pp. 19–26, 1981.
17. S. Y. Kung, VLSI Array Processor. Englewood Cliffs, NJ: Prentice-Hall, 1988.
18. P. E. Gill, G. H. Golub, W. Murray, and M. A. Saunders, “Methods for modifying matrix factorizations,” Math Comput., vol. 28, pp. 505–535, 1974.
19. A. P. Varvisiotis and S. Theodoridis, “A pipelined structure for QR adaptive LS system identification,” IEEE Trans. Signal Processing, vol. 39, no. 8, pp. 1920–1923, Aug. 1991.
20. G. E. Bottomley and S. T. Alexander, “A novel approach for stabilizing recursive least squares filters,” IEEE Trans. Signal Processing, vol. 39, no. 8, pp. 1770–1779, Aug. 1991.
21. A. L. Ghirnikar, S. T. Alexander, and R. J. Plemmons, “A parallel implementation of the inverse QR adaptive filter,” Comput. Elec. Eng., vol. 18, no. 3/4, pp. 291–300, 1992.