Polynomial Sparse Adaptive Estimation in Distributed Networks

Wireless sensor networks, including wireless acoustic sensor networks, have found applications in diverse areas including hearing aids, hands-free telephony, and target tracking. The objective of this brief is to introduce a new sparsity regularization parameter in sparse distributed network estimation, to achieve a better estimation accuracy in comparison with existing sparse-aware algorithms. In order to further reduce the computational complexity, the algorithm has also been designed for heterogeneous sensor networks, where only a fraction of the sensor nodes use sparse-aware adaptive estimation schemes.

[1]  Bijit Kumar Das,et al.  Sparse Adaptive Filtering by an Adaptive Convex Combination of the LMS and the ZA-LMS Algorithms , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Yuantao Gu,et al.  $l_{0}$ Norm Constraint LMS Algorithm for Sparse System Identification , 2009, IEEE Signal Processing Letters.

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

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

[5]  Alexander Bertrand,et al.  Applications and trends in wireless acoustic sensor networks: A signal processing perspective , 2011, 2011 18th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT).

[6]  PooGyeon Park,et al.  An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.

[7]  Jyoti Maheshwari,et al.  Polynomial sparse adaptive algorithm , 2016 .

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

[9]  Bijit Kumar Das,et al.  Sparse distributed learning via heterogeneous diffusion adaptive networks , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

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

[11]  Ali H. Sayed,et al.  Sparse Distributed Learning Based on Diffusion Adaptation , 2012, IEEE Transactions on Signal Processing.

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

[13]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[14]  Ali H. Sayed,et al.  Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks , 2012, IEEE Transactions on Signal Processing.

[15]  Bijit Kumar Das,et al.  Sparse Distributed Estimation via Heterogeneous Diffusion Adaptive Networks , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.