A Novel Normalized Sign Algorithm for System Identification Under Impulsive Noise Interference
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To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies.
KeywordsAdaptive filtering Convex combination Normalized sign algorithm System identification Impulsive noise
The authors want to express their deep thanks to the anonymous reviewers for many valuable comments which greatly helped to improve the quality of this work. This work was supported in part by National Natural Science Foundation of China (Grants: 61271340, 61571374, 61134002, 61433011, U1234203), the Sichuan Provincial Youth Science and Technology Fund (Grant: 2012JQ0046), and the Fundamental Research Funds for the Central Universities (Grant: SWJTU12CX026).
- 6.S. C. Douglas, Analysis and implementation of the max-NLMS adaptive filter, in Proceedings on 29th Asilomar Conference on Signals, Systems, and Computers, pp. 659–663 (1995)Google Scholar
- 9.B. E. Jun, D. J. Park, Y. W. Kim, Convergence analysis of sign-sign LMS algorithm for adaptive filters with correlated Gaussian data, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1380–1383 (1995)Google Scholar
- 12.S. Koike, Convergence analysis of adaptive filters using normalized sign-sign algorithm. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E88–A(11), 3218–3224 (2006)Google Scholar
- 15.L. Lu, H. Zhao, A novel convex combination of LMS adaptive filter for system identification, in 2014 12th International Conference on Signal Processing (ICSP), Hangzhou, pp. 225–229 (2014)Google Scholar
- 17.D. P. Mandic, E. V. Papoulis, C. G. Boukis, A normalized mixed-norm adaptive filtering algorithm robust under impulsive noise interference, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 333–336 (2003)Google Scholar
- 22.V. H. Nascimento, R. C. de Lamare, A low-complexity strategy for speeding up the convergence of convex combinations of adaptive filters, in IEEE International Conference on Acoustics, Speech and Signal Processing, pp 3553–3556 (2012)Google Scholar
- 26.A.H. Sayed, Fundamentals of Adaptive Filtering (Wiley IEEE Press, New York, 2003)Google Scholar
- 27.T. Shao, Y. R. Zheng, J. Benesty, A variable step-size normalized sign algorithm for acoustic echo cancelation, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 333–336 (2010)Google Scholar
- 32.P. Yuvapoositanon, J. Chambers, An adaptive step-size code-constrained minimum output energy receiver for nonstationary CDMA channels, in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 465–468 (2003)Google Scholar