A Diffusion Adaptive Network Algorithm for Robust Scalar Field Estimation in Wireless Sensor Networks

In this paper, a robust scalar filed estimation algorithm for wireless sensor networks that contain some nodes with very low quality observations, due to impulsive noise, is proposed. To derive the proposed algorithm, we first recast the robust scalar field estimation as an optimization problem with a maximum correntropy cost function. Then, we develop a distributed solution for it based on the diffusion adaptive network. We evaluate the performance of the proposed algorithm on a scalar field estimation problem and compare it with some similar algorithms, such as diffusion LMS algorithm. Simulation results show the superior performance of the proposed algorithm.

[1]  D. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .

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

[3]  Yih-Fang Huang,et al.  Distributed Least Mean-Square Estimation With Partial Diffusion , 2014, IEEE Transactions on Signal Processing.

[4]  Martin Vetterli,et al.  Sensor networks for diffusion fields: Detection of sources in space and time , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

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

[6]  Ali H. Sayed,et al.  Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data , 2011, IEEE Transactions on Signal Processing.

[7]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[8]  Martin Vetterli,et al.  Spatial super-resolution of a diffusion field by temporal oversampling in sensor networks , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Azam Khalili,et al.  Diffusion adaptive networks with imperfect communications: link failure and channel noise , 2014, IET Signal Process..

[10]  PooGyeon Park,et al.  A Variable Step-Size Diffusion Normalized Least-Mean-Square Algorithm with a Combination Method Based on Mean-Square Deviation , 2015, Circuits Syst. Signal Process..

[11]  Mihaela van der Schaar,et al.  Information-Sharing Over Adaptive Networks With Self-Interested Agents , 2014, IEEE Transactions on Signal and Information Processing over Networks.

[12]  Martin Vetterli,et al.  Sampling and reconstructing diffusion fields with localized sources , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Mohammad Ali Tinati,et al.  Steady-State Analysis of the Deficient Length Incremental LMS Adaptive Networks , 2015, Circuits Syst. Signal Process..

[14]  Ali H. Sayed,et al.  Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm , 2010, IEEE Transactions on Signal Processing.

[15]  Azam Khalili,et al.  Performance analysis of quantized incremental LMS algorithm for distributed adaptive estimation , 2010, Signal Process..

[16]  Ali Sayed,et al.  Adaptation, Learning, and Optimization over Networks , 2014, Found. Trends Mach. Learn..

[17]  Sergio Barbarossa,et al.  Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Azam Khalili,et al.  Derivation and analysis of incremental augmented complex least mean square algorithm , 2015, IET Signal Process..

[19]  Ali H. Sayed,et al.  Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks , 2011, IEEE Transactions on Signal Processing.

[20]  Azam Khalili,et al.  Steady-State Analysis of Diffusion LMS Adaptive Networks With Noisy Links , 2012, IEEE Transactions on Signal Processing.

[21]  Asrar U. H. Sheikh,et al.  A new LMS strategy for sparse estimation in adaptive networks , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[22]  D. Puccinelli,et al.  Wireless sensor networks: applications and challenges of ubiquitous sensing , 2005, IEEE Circuits and Systems Magazine.

[23]  Ali H. Sayed,et al.  Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior , 2013, IEEE Signal Processing Magazine.

[24]  José M. F. Moura,et al.  Dynamic Field Estimation Using Wireless Sensor Networks: Tradeoffs Between Estimation Error and Communication Cost , 2009, IEEE Transactions on Signal Processing.

[25]  Marc Moonen,et al.  Source localization and signal reconstruction in a reverberant field using the FDTD method , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

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

[27]  Azam Khalili,et al.  A Robust Diffusion Adaptive Network Based on the Maximum Correntropy Criterion , 2015, 2015 24th International Conference on Computer Communication and Networks (ICCCN).

[28]  Paolo Braca,et al.  Enforcing Consensus While Monitoring the Environment in Wireless Sensor Networks , 2008, IEEE Transactions on Signal Processing.

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

[30]  Gang George Yin,et al.  Distributed Energy-Aware Diffusion Least Mean Squares: Game-Theoretic Learning , 2013, IEEE Journal of Selected Topics in Signal Processing.