Diffusion adaptive filtering algorithm based on the Fair cost function

Document Type

Article

Publication Date

12-1-2021

Abstract

To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.

Identifier

85116470722 (Scopus)

Publication Title

Scientific Reports

External Full Text Location

https://doi.org/10.1038/s41598-021-99330-9

e-ISSN

20452322

PubMed ID

34611242

Issue

1

Volume

11

Grant

2021NQNCZ04

Fund Ref

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

This document is currently not available here.

Share

COinS