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.
Highlights
A Gentle Introduction to Optimization. 2018. p. 1–43. 18
We propose a robust distributed adaptive filtering algorithm by combining the Fair cost function and ATC scheme at all distributed network nodes in this paper, namely the DFair algorithm
We focus on the distributed adaptive filtering algorithm and compare the DFair algorithm with the RDLMS29, DNLMM30, DGCLD42, and D PLMS41 algorithms in linear system identification under different types of input signal and impulsive interference
Summary
A Gentle Introduction to Optimization. 2018. p. 1–43. 18. Guan, S. & Li, Z. We propose a robust distributed adaptive filtering algorithm by combining the Fair cost function and ATC scheme at all distributed network nodes in this paper, namely the DFair algorithm.
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