Sciweavers

ACSSC
2015

Sparsity-aware distributed conjugate gradient algorithms for parameter estimation over sensor networks

7 years 11 months ago
Sparsity-aware distributed conjugate gradient algorithms for parameter estimation over sensor networks
— This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using l1 and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean square deviation (MSD) and convergence as compared with the consensus least-mean square (Diffusion-LMS) algorithm, the diffusion CG algorithms and a close performance to the diffusion distributed recursive least squares (Consensus-RLS) algorithm. Numerical results show that the proposed algorithms are reliable and can be applied in several scenarios. Keywords— Distributed Processing, Diffusion Strategy, Conjugate Gradient, Sparsity Aware.
Tamara Guerra Miller, Songcen Xu, Rodrigo C. de La
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACSSC
Authors Tamara Guerra Miller, Songcen Xu, Rodrigo C. de Lamare, Vitor H. Nascimento, Yuriy V. Zakharov
Comments (0)