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Telfor Journal
2016, vol. 8, br. 1, str. 32-37
jezik rada: engleski
vrsta rada: neklasifikovan

An affine combination of adaptive filters for channels with different sparsity levels
(naslov ne postoji na srpskom)
Department of Radio and Communication Engineering, Tallinn University of Technology, Tallinn, Estonia



(ne postoji na srpskom)
In this paper we present an affine combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows a better performance than each of the combining filters separately. We also demonstrate that affine combination results in faster convergence than a convex combination of two adaptive filters.

Ključne reči

adaptive filters; combination filters; sparse impulse response


Arenas-Garcia, J., Figueiras-Vidal, A.R. (2008) Adaptive combination of IPNLMS filters for robust sparse echo cancellation. u: IEEE Workshop on Machine Learning for Signal Processing, pp. 221-226, October
Benesty, J., Gay, S.L. (2002) An improved PNLMS algorithm. u: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, May
Bershad, N.J., Bermudez, J.C.M., Tourneret, J.-Y. (2008) An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis. IEEE Transactions on Signal Processing, 56(5): 1853-1864
Butsenko, M., Trump, T. (2015) An affine combination of adaptive filters for sparse impulse response identification. u: 23rd. Telecommunications Forum Telfor (TELFOR), Belgrade, pp. 396-399
Chen, Y., Gu, Y., Hero, A.O. (2009) Sparse LMS for system identification. u: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3125-3128, April
Das, B.K., Chakraborty, M. (2014) Sparse Adaptive Filtering by an Adaptive Convex Combination of the LMS and the ZA-LMS Algorithms. IEEE Transactions on Circuits and Systems I: Regular Papers, 61(5): 1499-1507
Duttweiler, D.L. (2000) Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing, 8(5): 508-518
Gay, S.L. (1998) An efficient, fast converging adaptive filter for network echo cancellation. u: Conference Record of the Thirty-Second Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 394-398, November
Gu, Y., Jin, J., Mei, S. (2009) ‘l0 norm constraint LMS algorithm for sparse system identification. IEEE Signal Processing Letters, 16(9): 774-777
Gui, G., Kumagai, S., Mehbodniya, A., Adachi, F. (2014) Two are better than one: Adaptive sparse system identification using affine combination of two sparse adaptive filters. u: IEEE 79th Vehicular Technology Conference, pp. 1-5, May
Haykin, S. (2002) Adaptive Filter Theory. Prentice Hall, Fourth Edition
Hoyer, P.O. (2004) Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, vol. 5, pp. 1457-1469, November
ITU-T (2012) Digital network echo cancellers. Rec. G. 168
Khong, A.W.H., Naylor, P.A. (2006) Efficient use of sparse adaptive filters. u: Fortieth Asilomar Conference on Signals, Systems and Computers, pp. 1375-1379, October-November
Trump, T. (2011) A combination of two NLMS filters in an adaptive line enhancer. u: 17th International Conference on Digital Signal Processing (DSP), pp. 1-6, July 2011
Vaseghi, S.V. (2008) Advanced Digital Signal Processing and Noise Reduction. John Wiley & Sons, Fourth Edition