Robust diffusion maximum correntropy criterion algorithm for distributed network estimation

Robust diffusion algorithms based on the maximum correntropy criterion(MCC) are developed to address the distributed networks estimation issue in impulsive(long-tailed) noise environments. The cost functions used in distributed network estimation are in general based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. In non-Gaussian situations, such as the impulsive-noise case, MCC based method may achieve a much better performance than the MSE methods since it takes into account higher order statistics of error distribution. The proposed methods can also outperform the robust diffusion least mean p-power(DLMP) and diffusion minimum error entropy (DMEE) algorithms.

[1]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[2]  Zhaoyang Zhang,et al.  Diffusion Sparse Least-Mean Squares Over Networks , 2012, IEEE Transactions on Signal Processing.

[3]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[4]  Zhaoyang Zhang,et al.  Diffusion Information Theoretic Learning for Distributed Estimation Over Network , 2013, IEEE Transactions on Signal Processing.

[5]  Jae-Woo Lee,et al.  A Variable Step-Size Diffusion LMS Algorithm for Distributed Estimation , 2015, IEEE Transactions on Signal Processing.

[6]  LiDefang,et al.  Robust locality preserving projection based on maximum correntropy criterion , 2014 .

[7]  Gregory J. Pottie,et al.  Instrumenting the world with wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[8]  Azzedine Zerguine,et al.  A variable step-size strategy for distributed estimation over adaptive networks , 2013, EURASIP Journal on Advances in Signal Processing.

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

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

[11]  Isao Yamada,et al.  Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis , 2010, IEEE Transactions on Signal Processing.

[12]  Jose C. Principe,et al.  Information Theoretic Learning - Renyi's Entropy and Kernel Perspectives , 2010, Information Theoretic Learning.

[13]  Jian Yang,et al.  Recursive robust least squares support vector regression based on maximum correntropy criterion , 2012, Neurocomputing.

[14]  Jiashu Zhang,et al.  Robust locality preserving projection based on maximum correntropy criterion , 2014, J. Vis. Commun. Image Represent..

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

[16]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

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

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

[19]  Fuxi Wen,et al.  Diffusion Least Mean P-Power Algorithms for Distributed Estimation in Alpha-Stable Noise Environments , 2013, ArXiv.

[20]  Hong-Jie Xing,et al.  Regularized correntropy criterion based feature extraction for novelty detection , 2014, Neurocomputing.

[21]  Hua Qu.  A New Learning Algorithm Based on MCC for Colored Noise Interference Cancellation , 2013 .

[22]  Ali H. Sayed,et al.  Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis , 2008, IEEE Transactions on Signal Processing.

[23]  Ali H. Sayed,et al.  Diffusion recursive least-squares for distributed estimation over adaptive networks , 2008, IEEE Transactions on Signal Processing.

[24]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[25]  Azzedine Zerguine,et al.  Convergence and steady-state analysis of a variable step-size NLMS algorithm , 2003, Signal Process..

[26]  Ali H. Sayed,et al.  Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.

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

[28]  Ali H. Sayed,et al.  Fundamentals Of Adaptive Filtering , 2003 .

[29]  Jae-Woo Lee,et al.  Data-selective diffusion LMS for reducing communication overhead , 2015, Signal Process..

[30]  Bhaskar Krishnamachari,et al.  Distributed parameter estimation for monitoring diffusion phenomena using physical models , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..