A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

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.

[1]  Andy W. H. Khong,et al.  Efficient Use Of Sparse Adaptive Filters , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[2]  Mariane R. Petraglia,et al.  A unified approach for sparsity-aware and maximum correntropy adaptive filters , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[3]  Yanyan Wang,et al.  Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm , 2017, Wirel. Commun. Mob. Comput..

[4]  Zeljko Zilic,et al.  Echo cancellation in IP networks , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[5]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[6]  Shigang Wang,et al.  Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification , 2015, Circuits, Systems, and Signal Processing.

[7]  Yanyan Wang,et al.  Sparse Multipath Channel Estimation Using Norm Combination Constrained Set-Membership NLMS Algorithms , 2017, Wirel. Commun. Mob. Comput..

[8]  Badong Chen,et al.  Maximum Correntropy Estimation Is a Smoothed MAP Estimation , 2012, IEEE Signal Processing Letters.

[9]  Donald L. Duttweiler,et al.  Proportionate normalized least-mean-squares adaptation in echo cancelers , 2000, IEEE Trans. Speech Audio Process..

[10]  Jingdong Chen,et al.  Acoustic MIMO Signal Processing , 2006 .

[11]  Isao Yamada,et al.  A sparse adaptive filtering using time-varying soft-thresholding techniques , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Seyed Mojtaba Atarodi,et al.  A fast converging algorithm for network echo cancellation , 2004, IEEE Signal Processing Letters.

[13]  Yanyan Wang,et al.  Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels , 2017, Entropy.

[14]  Jacob Benesty,et al.  Sparse Adaptive Filters for Echo Cancellation , 2010, Synthesis Lectures on Speech and Audio Processing.

[15]  Aníbal R. Figueiras-Vidal,et al.  Adaptive Combination of Proportionate Filters for Sparse Echo Cancellation , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Abolfazl Mehbodniya,et al.  Least mean square/fourth algorithm for adaptive sparse channel estimation , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[17]  Yanyan Wang,et al.  Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation , 2016 .

[18]  Yanyan Wang,et al.  A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification , 2017, Entropy.

[19]  Mike Brookes,et al.  Adaptive algorithms for sparse echo cancellation , 2006, Signal Process..

[20]  Mohammad Shukri Salman,et al.  Sparse leaky‐LMS algorithm for system identification and its convergence analysis , 2014 .

[21]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[22]  José Carlos Príncipe,et al.  Using Correntropy as a cost function in linear adaptive filters , 2009, 2009 International Joint Conference on Neural Networks.

[23]  Nanning Zheng,et al.  Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion , 2015, IEEE Signal Processing Letters.

[24]  Konstantinos Pelekanakis,et al.  Comparison of sparse adaptive filters for underwater acoustic channel equalization/Estimation , 2010, 2010 IEEE International Conference on Communication Systems.

[25]  Nanning Zheng,et al.  Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion , 2014, IEEE Signal Processing Letters.

[26]  Tao Jiang,et al.  Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation , 2016, Signal Process..

[27]  Nanning Zheng,et al.  Generalized Correntropy for Robust Adaptive Filtering , 2015, IEEE Transactions on Signal Processing.

[28]  Fumiyuki Adachi,et al.  Improved adaptive sparse channel estimation based on the least mean square algorithm , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[29]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[30]  Masanori Hamamura,et al.  Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation , 2015 .

[31]  Yingsong Li,et al.  An Improved Proportionate Normalized Least-Mean-Square Algorithm for Broadband Multipath Channel Estimation , 2014, TheScientificWorldJournal.

[32]  Tao Jiang,et al.  Sparse least mean mixed‐norm adaptive filtering algorithms for sparse channel estimation applications , 2017, Int. J. Commun. Syst..

[33]  Wentao Ma,et al.  Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments , 2015, J. Frankl. Inst..

[34]  Zongze Wu,et al.  Proportionate Minimum Error Entropy Algorithm for Sparse System Identification , 2015, Entropy.

[35]  Bhaskar D. Rao,et al.  Sparse channel estimation via matching pursuit with application to equalization , 2002, IEEE Trans. Commun..

[36]  Yanyan Wang,et al.  Sparse adaptive channel estimation based on mixed controlled l2 and lp-norm error criterion , 2017, J. Frankl. Inst..

[37]  S.L. Gay,et al.  An efficient, fast converging adaptive filter for network echo cancellation , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[38]  Steve Rogers,et al.  Adaptive Filter Theory , 1996 .

[39]  Badong Chen,et al.  Kernel adaptive filtering with maximum correntropy criterion , 2011, The 2011 International Joint Conference on Neural Networks.

[40]  P. Vainikainen,et al.  Measurement of Large-Scale Cluster Power Characteristics for Geometric Channel Models , 2007, IEEE Transactions on Antennas and Propagation.

[41]  Lee Freitag,et al.  Channel-estimation-based adaptive equalization of underwater acoustic signals , 1999, Oceans '99. MTS/IEEE. Riding the Crest into the 21st Century. Conference and Exhibition. Conference Proceedings (IEEE Cat. No.99CH37008).

[42]  Yanyan Wang,et al.  Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation , 2017, Symmetry.

[43]  Patrick A. Naylor,et al.  An improved IPNLMS algorithm for echo cancellation in packet-switched networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[44]  Alfred O. Hero,et al.  Sparse LMS for system identification , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[45]  Yingsong Li,et al.  Sparse SM-NLMS algorithm based on correntropy criterion , 2016 .

[46]  James A. Rodger,et al.  Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS , 2012, Expert Syst. Appl..

[47]  Yanyan Wang,et al.  A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).