Robust incremental adaptive strategies for distributed networks to handle outliers in both input and desired data

Conventional distributed strategies based on least error squares cost function are not robust against outliers present in the desired and input data. This manuscript employs the generalized-rank (GR) technique as a cost function instead of least error squares cost function to control the effects of outliers present both in input and desired data. A novel indicator function and median based approach are proposed to decrease the computational complexity requirement at the sensor nodes. Further to increase the convergence speed a sign regressor GR norm is also proposed and used. Simulation based experiments show that the performance obtained using proposed methods is robust against outliers in the desired and input data.

[1]  T. Hettmansperger,et al.  Robust Nonparametric Statistical Methods , 1998 .

[2]  E. Alper Yildirim,et al.  On the Minimum Volume Covering Ellipsoid of Ellipsoids , 2006, SIAM J. Optim..

[3]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[4]  Ganapati Panda,et al.  Robust identification using new Wilcoxon least mean square algorithm , 2009 .

[5]  Piyush Kumar,et al.  Approximate Minimum Volume Enclosing Ellipsoids Using Core Sets , 2003 .

[6]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[7]  V. Yohai,et al.  Robust Statistics: Theory and Methods , 2006 .

[8]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[9]  Yih-Lon Lin,et al.  Preliminary Study on Wilcoxon Learning Machines , 2008, IEEE Transactions on Neural Networks.

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

[11]  A.H. Sayed,et al.  Distributed Recursive Least-Squares Strategies Over Adaptive Networks , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[12]  Marc Moonen,et al.  Consensus-Based Distributed Total Least Squares Estimation in Ad Hoc Wireless Sensor Networks , 2011, IEEE Transactions on Signal Processing.

[13]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[14]  Upendra Kumar Sahoo,et al.  QR-based incremental minimum-Wilcoxon-norm strategies for distributed wireless sensor networks , 2012, Signal Process..

[15]  Ali H. Sayed,et al.  Diffusion Least-Mean Squares Over Adaptive Networks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[16]  Piyush Kumar,et al.  Minimum-Volume Enclosing Ellipsoids and Core Sets , 2005 .

[17]  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).

[18]  Ali H. Sayed,et al.  Incremental Adaptive Strategies Over Distributed Networks , 2007, IEEE Transactions on Signal Processing.

[19]  K. Furutsu,et al.  On the Theory of Amplitude Distribution of Impulsive Random Noise , 1961 .

[20]  Joseph W. McKean,et al.  Robust Analysis of Linear Models , 2004 .

[21]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.