×

A general zero attraction proportionate normalized maximum correntropy criterion algorithm for sparse system identification. (English) Zbl 1423.93094

Summary: A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations.

MSC:

93B30 System identification
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Khong, A.W.H.; Naylor, P.A.; Efficient use of sparse adaptive filters; Proceedings of the Fortieth Asilomar Conference on Signals, Systems and Computers (ACSSC ’06): ; ,1375-1379.
[2] Paleologu, C.; Benesty, J.; Ciochina, S.; ; Sparse Adaptive Filters for Echo Cancellation: San Rafael, CA, USA 2010; . · Zbl 1235.94002
[3] Rodger, J.A.; Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS; Expert Syst. Appl.: 2012; Volume 139 ,9821-9836.
[4] Murakami, Y.; Yamagishi, M.; Yukawa, M.; Yamada, I.; A sparse adaptive filtering using time-varying soft-thresholding techniques; Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP): ; ,3734-3737.
[5] Li, Y.; Hamamura, M.; Zero-attracting variable-step-size least mean square algorithms for adaptive sparse channel estimation; Int. J. Adapt. Control Signal Process.: 2015; Volume 29 ,1189-1206. · Zbl 1330.93248
[6] Chen, Y.; Gu, Y.; Hero, A.O.; Sparse LMS for system identification; Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’09): ; ,3125-3128.
[7] Wang, Y.; Li, Y.; Yang, R.; Sparse adaptive channel estimation based on mixed controlled l2 and lp-norm error criterion; J. Frankl. Inst.: 2017; Volume 354 ,7215-7239. · Zbl 1373.93333
[8] Li, Y.; Wang, Y.; Jiang, T.; Sparse least mean mixed-norm adaptive filtering algorithms for sparse channel estimation applications; Int. J. Commun. Syst.: 2017; Volume 30 ,1-16.
[9] Wang, Y.; Li, Y.; Sparse multi-path channel estimation using norm combination constrained set-membership NLMS algorithms; Wirel. Commun. Mob. Comput.: 2017; Volume 2017 ,8140702.
[10] Li, Y.; Jin, Z.; Wang, Y.; Adaptive channel estimation based on an improved norm constrained set-membership normalized least mean square algorithm; Wirel. Commun. Mob. Comput.: 2017; Volume 2017 ,8056126.
[11] Duttweiler, D.L.; Proportionate normalized least-mean-squares adaptation in echo cancelers; IEEE Trans. Speech Audio Process.: 2000; Volume 8 ,508-518.
[12] Naylor, P.A.; Cui, J.; Brookes, M.; Adaptive algorithms for sparse echo cancellation; Signal Process.: 2009; Volume 86 ,1182-1192. · Zbl 1163.94366
[13] Cotter, S.F.; Rao, B.D.; Sparse channel estimation via matching pursuit with application to equalization; IEEE Trans. Commun.: 2002; Volume 50 ,374-377.
[14] Gui, G.; Peng, W.; Adachi, F.; Improved adaptive sparse channel estimation based on the least mean square algorithm; Proceedings of the 2013 IEEE Wireless Communications and Networking Conference (WCNC): ; ,3105-3109.
[15] Arenas, J.; Figueiras-Vidal, A.R.; Adaptive combination of proportionate filters for sparse echo cancellation; IEEE Trans. Audio Speech Lang. Process.: 2009; Volume 17 ,1087-1098.
[16] Nekuii, M.; Atarodi, M.; A fast converging algorithm for network echo cancellation; IEEE Signal Process. Lett.: 2004; Volume 11 ,427-430.
[17] Li, Y.; Wang, Y.; Jiang, T.; Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation; Signal Process.: 2016; Volume 128 ,243-251.
[18] Li, Y.; Zhang, C.; Wang, S.; Low-complexity non-uniform penalized affine projection algorithm for sparse system identification; Circuits Syst. Signal Process.: 2016; Volume 35 ,1611-1624. · Zbl 1346.93124
[19] Stojanovic, M.; Freitag, L.; Johnson, M.; Channel-estimation-based adaptive equalization of underwater acoustic signals; Proceedings of the OCEANS ’99 MTS/IEEE, Riding the Crest into the 21st Century: ; ,985-990.
[20] Pelekanakis, K.; Chitre, M.; Comparison of sparse adaptive filters for underwater acoustic channel equalization/estimation; Proceedings of the 2010 IEEE International Conference on Communication Systems (ICCS): ; ,395-399.
[21] Li, Y.; Wang, Y.; Jiang, T.; Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation; AEU Int. J. Electron. Commun.: 2016; Volume 70 ,895-902.
[22] Gui, G.; Mehbodniya, A.; Adachi, F.; Least mean square/fourth algorithm for adaptive sparse channel estimation; Proceedings of the 2013 IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC): ; ,296-300.
[23] Vuokko, L.; Kolmonen, V.M.; Salo, J.; Vainikainen, P.; Measurement of large-scale cluster power characteristics for geometric channel models; IEEE Trans. Antennas Propagat.: 2007; Volume 55 ,3361-3365.
[24] Radecki, J.; Zilic, Z.; Radecka, K.; Echo cancellation in IP networks; Proceedings of the 45th Midwest Symposium on Circuits and Systems: ; ,219-222.
[25] Cui, J.; Naylor, P.A.; Brown, D.T.; An improved IPNLMS algorithm for echo cancellation in packet-switched networks; Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’04): ; .
[26] Widrow, B.; Stearns, S.D.; ; Adaptive Signal Processing: Upper Saddle River, NJ, USA 1985; . · Zbl 0593.93063
[27] Wang, Y.; Li, Y.; Norm penalized joint-optimization NLMS algorithms for broadband sparse adaptive channel estimation; Symmetry: 2017; Volume 9 . · Zbl 1423.94026
[28] Haykin, S.; ; Adaptive Filter Theory: Upper Saddle River, NJ, USA 1991; . · Zbl 0723.93070
[29] Li, Y.; Hamamura, M.; An improved proportionate normalized least-mean-square algorithm for broadband multipath channel estimation; Sci. World J.: 2014; Volume 2014 ,572969.
[30] Benesty, J.; Gay, S.L.; An improved PNLMS algorithm; Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP): ; Volume Volume II ,1881-1884.
[31] Gay, S.L.; An efficient, fast converging adaptive filter for network echo cancellation; Proceedings of the 32nd Asilomar Conference on Signals and System for Computing: ; Volume Volume 1 ,394-398.
[32] Liu, W.F.; Pokharel, P.P.; Principe, J.C.; Correntropy: Properties and applications in non-Gaussian signal processing; IEEE Trans. Signal Process.: 2007; Volume 55 ,5286-5298. · Zbl 1390.94277
[33] Li, Y.; Wang, Y.; Sparse SM-NLMS algorithm based on correntropy criterion; Electron. Lett.: 2016; Volume 52 ,1461-1463.
[34] Chen, B.; Principe, J.C.; Maximum correntropy estimation is a smoothed MAP estimation; IEEE Signal Process. Lett.: 2012; Volume 19 ,491-494.
[35] Chen, B.; Xing, L.; Liang, J.; Zheng, N.; Principe, J.C.; Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion; IEEE Signal Process. Lett.: 2014; Volume 21 ,880-884.
[36] Zhao, S.; Chen, B.; Principe, J.C.; Kernel adaptive filtering with maximum correntropy criterion; Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN): ; ,2012-2017.
[37] Chen, B.; Xing, L.; Zhao, H.; Zheng, N.; Principe, J.C.; Generalized correntropy for robust adaptive filtering; IEEE Trans. Signal Process.: 2016; Volume 64 ,3376-3387. · Zbl 1414.94113
[38] Chen, B.; Wang, J.; Zhao, H.; Zheng, N.; Principe, J.C.; Convergence of a fixed-point algorithm under maximum correntropy criterion; IEEE Signal Process. Lett.: 2015; Volume 22 ,1723-1727.
[39] Singh, A.; Principe, J.C.; Using correntropy as a cost function in linear adaptive filters; Proceedings of the International Joint Conference on Neural Networks: ; ,2950-2955.
[40] Hadded, D.B.; Petraglia, M.R.; Petraglia, A.; A unified approach for sparsity-aware and maximum correntropy adaptive filters; Proceedings of the 24th European Signal Processing Conference (EUSIPCO): ; ,170-174.
[41] Ma, W.; Qu, H.; Gui, G.; Xu, L.; Zhao, J.; Chen, B.; Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments; J. Frankl. Inst.: 2015; Volume 352 ,2708-2727. · Zbl 1395.93544
[42] Wang, Y.; Li, Y.; Albu, F.; Yang, R.; Group-constrained maximum correntropy criterion algorithms for estimating sparse mix-noised channels; Entropy: 2017; Volume 19 .
[43] Wu, Z.; Peng, S.; Chen, B.; Zhao, H.; Principe, J.C.; Proportionate minimum error entropy algorithm for sparse system identification; Entropy: 2015; Volume 17 ,5995-6006. · Zbl 1338.94049
[44] Salman, M.S.; Sparse leaky-LMS algorithm for system identification and its convergence analysis; Int. J. Adapt. Control Signal Process.: 2014; Volume 28 ,1065-1072. · Zbl 1337.93093
[45] Li, Y.; Wang, Y.; Yang, R.; Albu, F.; A soft parameter function penalized normalized maximum correntropy criterion algorithm for sparse system identification; Entropy: 2017; Volume 19 .
[46] Wang, Y.; Li, Y.; Yang, R.; A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-gaussian environment; Proceedings of the 25th European Signal Processing Conference (EUSIPCO): ; ,246-250.
[47] Hoyer, P.O.; Non-negative matrix factorization with sparseness constraints; J. Mach. Learn. Res.: 2001; Volume 49 ,1208-1215.
[48] Huang, Y.; Benesty, J.; Chen, J.; ; Acoustic MIMO Signal Processing: Berlin, Germany 2006; . · Zbl 1106.94003
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.