Least lp-norm impulsive noise cancellation with polynomial filters

Signal Processing

Volumn 69, Issue 1, 1998, Pages 1-14

  Kuruoǧlu, Ercan E ^a,b   Rayner, Peter J W ^a   Fitzgerald, William J ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b Xerox Research Centre Europe   (United Kingdom)

Author keywords

Impulsive noise; Iteratively reweighted least squares; Least lp norm estimation; Non gaussian p.d.f; Signal to noise ratio; Volterra filters; stable distribution

Indexed keywords

ALGORITHMS; POLYNOMIALS; SIGNAL DISTORTION; SIGNAL FILTERING AND PREDICTION; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; STATISTICS;

GAUSSIAN DISTRIBUTION; GENERALISED CENTRAL LIMIT THEOREM; IMPULSIVE NOISE CANCELLATION;

SPURIOUS SIGNAL NOISE;

EID: 0032132313     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1684(98)00083-8     Document Type: Article

References (33)

1. O. Arikan, A.E. Çetin, E. Erzin, Adaptive filtering for non-Gaussian stable processes, IEEE Signal Process. Lett. 1 (11) (November 1994) 163-165.
2. R.H. Byrd, D.A. Pyne, Convergence of the iteratively reweighted least squares algorithm for robust regression, Tech. Rep. 313, Dept. of Mathematical Sciences, John Hopkins University, 1979.
3. S. Cambanis, G. Miller, Linear problems in pth-order and stable processes, SIAM J. Appl. Math. 41 (1) (1981) 43-69.
4. W. Feller, An Introduction to Probability Theory and Its Applications, Vol. II, Wiley, New York, 1966.
5. S.J. Godsill, P.J.W. Rayner, A Bayesian approach to the restoration of degraded audio signals, IEEE Trans. Speech Audio Process. 3 (4) (July 1995) 267-278.
6. H. Hall, A new model for impulsive phenomena: application to atmospheric-noise communication channel, Technical Report 3412-8, 66-052, Stanford University, 1966.
7. J. Ilow, Signal processing in α-stable noise environments: Noise modeling, detection and estimation, Ph.D. Thesis, University of Toronto, 1995.
8. L. Izzo, L. Panico, L. Paura, Error rates for fading NCFSK signals in an additive mixture of impulsive and Gaussiannoise, IEEE Trans. Commun. 30 (11) (1982) 2434-2438.
9. S.R. Kim, A. Efron, Adaptive robust impulsive noise filtering, IEEE Trans. Signal Process. 43 (8) (August 1995) 1855-1866.
10. T. Koh, E.J. Powers, Second-order Volterra filtering and its application to nonlinear system identification, IEEE Trans. Acoust. Speech Signal Process. 33 (6) (December 1985) 1445-1455.
11. H.M. Kim, B. Kosko, Fuzzy prediction and filtering in impulsive noise, Fuzzy Sets and Systems 77 (1) (1996) 15-33.
12. E.E. Kuruoglu, Signal estimation in non-Gaussian noise using polynomial filters, M.Phil. Thesis, University of Cambridge, Department of Engineering, Signal Processing and Communications Laboratory, July 1995.
13. p-norm estimation of autoregressive model coefficients of symmetric α-stable processes, IEEE Signal Process. Lett. 4 (7) (July 1997) 201-203.
14. X. Ma, C.L. Nikias, Parameter estimation and blind channel identification in impulsive signal environments, IEEE Trans. Signal Process. 43 (12) (December 1995) 2884-2897.
15. X. Ma, C.L. Nikias, Joint estimation of time-delay and frequency delay in impulsive noise using fractional lower order-statistics, IEEE Trans. Signal Process. 44 (11) (1996) 2669-2687.
16. P. Mertz, Model of impulsive noise for data transmission, IRE Trans. Commun. Systems 9 (6) (1961) 130-137.
17. D. Middleton, Statistical-physical models of electromagnetic interference, IEEE Trans. Electromagnetic Compatibility EMC-19 (3) (1977) 106-127.
18. p-norm estimation and the choice of p, Communications in Statist. Simulation Comput. 11 (1) (1982) 89-109.
19. C.L. Nikias, M. Shao, Signal Processing with α-Stable Distributions and Applications, Wiley, New York, 1995.
20. A. Papoulis, Probability Random Variables and Stochastic Processes, McGraw-Hill, New York, 1991.
21. I. Pitas, A.N. Venetsanopoulos, Order-statistics in digital image-processing, Proc. IEEE 80 (12) (December 1992) 1893-1921.
22. G. Samorodnitsky, M.S. Taqqu, Stable Non-Gaussian Rondom Processes: Stochastic Models with Infinite Variance, Chapman & Hall, New York (1994).
23. M. Schetzen, The Volterra and Wiener Theories of Nonlinear Systems, Wiley, New York, 1980.
24. p normed minimization with applications to linear predictive modeling for sinusoidal frequency estimation, Signal Processing 24 (2) (1991) 193-216.
25. D. Sengupta, S.M. Kay, Parameter estimation and GLRT detection in colored non-Gaussian autoregressive processes, IEEE Trans. Signal Process. 38 (10) (October 1990) 1661-1676.
26. M. Shao, C.L. Nikias, Signal processing with fractional lower order moments: stable processes and their applications, Proc. IEEE. 81 (7) (July 1993) 986-1010.
27. B.W. Stuck, Minimum error dispersion linear filtering of scalar symmetric stable processes, IEEE Trans. Automat. Control AC-23 (3) (June 1978) 507-509.
28. P. Tsakalides, C.L. Nikias, The robust covariation-based music (ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments, IEEE Trans. Signal Process. 44 (7) (1996) 1623-1633.
29. G.A. Tsihrintzis, C.L. Nikias, Fast estimation of the parameters of alpha-stable impulsive interference, IEEE Trans. Signal Process. 44 (6) (June 1996) 1492-1503.
30. D.W. Tufts, I.P. Kirsteins, P.F. Swaszek, A.J. Efron, C.D. Melissinos, Detection of signals in the presence of strong, signal-like interference and impulse noise, in: E.J. Wegman, S.C. Schwartz, J.B. Thomas (Eds.), Topics in Non-Gaussian Signal Processing, Springer, New York, 1989, pp. 184-196.
31. S.V. Vaseghi, P.J.W. Rayner, Detection and suppression of impulsive noise in speech communication systems, IEE Proc. Part I 137 (1) (February 1990) 38-46.
32. S.M. Zabin, G.A. Wright, Nonparametric density-estimation and detection in impulsive interference channels .1. estimators, IEEE Trans. Commun. 42 (2-4 Pt 3) (1994) 1684-1697.
33. V.M. Zolotarev, Mellin-Stieltjes transforms in probability theory, Theory Probab. Appl. 2 (4) (1957) 433-460.