Details

Autor(en) / Beteiligte

• Zayyani, Hadi
• Javaheri, Amirhossein

Titel

A Robust Generalized Proportionate Diffusion LMS Algorithm for Distributed Estimation

Ist Teil von

• IEEE transactions on circuits and systems. II, Express briefs, 2021-04, Vol.68 (4), p.1552-1556

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• This brief paper proposes a robust generalized proportionate diffusion Least Mean Square (LMS) algorithm for distributed estimation of a parameter vector in a network. The contribution of this brief is twofold. First, we generalize the concept of proportionate diffusion LMS by letting the gain matrix to be non-diagonal instead of being a diagonal matrix. Second, to achieve robustness to impulsive noise while simultaneously maintaining a fast convergence property, we use a combination of Mean Square Deviation (MSD) and disturbance incurred in the adaptation step as the objective cost function. By simplifying and optimizing the proposed cost function, a closed form formula is obtained for the gain matrix in the general non-diagonal case. Simulation results demonstrate the efficiency of the proposed method in comparison to some other state-of-the-art algorithms.