Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications

A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. Simulation results indicate that the HNC-PNMCC is better than the PNMCC, MCC, and sparse MCC with respect to the estimation performance for the cluster-sparse system identification under the impulsive noises.

[1]  Wei Yi,et al.  Approximation to independent lognormal sum with α-μ distribution and the application , 2015, Signal Process..

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

[3]  Jianming Liu,et al.  Proportionate Adaptive Filtering for Block-Sparse System Identification , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[4]  Yuantao Gu,et al.  Block-Sparsity-Induced Adaptive Filter for Multi-Clustering System Identification , 2014, IEEE Transactions on Signal Processing.

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

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

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

[8]  José M. N. Leitão,et al.  A DSP based long distance echo canceller using short length centered adaptive filters , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[10]  Teresa H. Y. Meng,et al.  Normalized data nonlinearities for LMS adaptation , 1994, IEEE Trans. Signal Process..

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

[12]  Binwei Weng,et al.  Nonlinear system identification in impulsive environments , 2005, IEEE Transactions on Signal Processing.

[13]  Milos Doroslovacki,et al.  Improving convergence of the PNLMS algorithm for sparse impulse response identification , 2005, IEEE Signal Processing Letters.

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

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

[16]  Jacob Benesty,et al.  On Regularization in Adaptive Filtering , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[17]  Jacob Benesty,et al.  Proportionate Adaptive Filters From a Basis Pursuit Perspective , 2010, IEEE Signal Processing Letters.

[18]  Yanyan Wang,et al.  An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation , 2017, Entropy.

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

[20]  Deniz Erdogmus,et al.  Generalized information potential criterion for adaptive system training , 2002, IEEE Trans. Neural Networks.

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

[22]  Patrick A. Naylor,et al.  A Partitioned Block Proportionate Adaptive Algorithm for Acoustic Echo Cancellation , 2010 .

[23]  Tao Jiang,et al.  Sparse channel estimation based on a p-norm-like constrained least mean fourth algorithm , 2015, 2015 International Conference on Wireless Communications & Signal Processing (WCSP).

[24]  Jingwei Yin,et al.  Cluster-Sparse Proportionate NLMS Algorithm With the Hybrid Norm Constraint , 2018, IEEE Access.

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

[26]  Jacob Benesty,et al.  A widely linear model for stereophonic acoustic echo cancellation , 2013, Signal Process..

[27]  Jingwei Yin,et al.  Mixed Norm Constrained Sparse APA Algorithm for Satellite and Network Echo Channel Estimation , 2018, IEEE Access.

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

[29]  Badong Chen,et al.  Robust Constrained Adaptive Filtering Under Minimum Error Entropy Criterion , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

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

[31]  Sheng Zhang,et al.  Robust Shrinkage Normalized Sign Algorithm in an Impulsive Noise Environment , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.

[32]  Peter J. McLane,et al.  PSK and DPSK trellis codes for fast fading, shadowed mobile satellite communication channels , 1988, IEEE Trans. Commun..

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

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