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

Tamara Guerra Miller, Songcen Xu, Rodrigo C. de Lamare, Vítor H. Nascimento, Yuriy V. Zakharov

Abstract: This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We develop 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 rate 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 (Diffusion-RLS) algorithm. Numerical results show that the proposed algorithms are reliable and can be applied in several scenarios.