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

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.