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A diffusion strategy for robust distributed estimation based on streaming graph signals.

Xinyu Li¹, Feng Chen², Qing Shi²

¹School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.

ISA Transactions

|June 22, 2023

Summary

This summary is machine-generated.

This study introduces a diffusion Mixture Correntropy (d-MC) algorithm for robust distributed estimation over dynamic graph signals. The new method effectively handles both Gaussian and impulsive noise, outperforming existing approaches.

Keywords:

Correntropy Distributed estimation Robustness Signal processing

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Area of Science:

Signal Processing
Network Science
Machine Learning

Background:

Distributed estimation over dynamic graph signals is crucial but existing methods struggle with non-Gaussian noise.
Mean-Square-Error (MSE) based approaches are sensitive to impulsive or non-Gaussian disturbances.

Purpose of the Study:

To develop a robust distributed estimation algorithm for dynamic and streaming graph signals.
To address the limitations of existing methods in handling non-Gaussian noise environments.

Main Methods:

A novel diffusion Mixture Correntropy (d-MC) algorithm is proposed.
The algorithm incorporates a diffusion strategy and a new cost function based on Mixture Correntropy.
Theoretical analysis of mean and mean-square stability is conducted.

Main Results:

The proposed d-MC algorithm accurately estimates graph filter parameters from dynamic and streaming graph signals.
The algorithm demonstrates robust performance in the presence of both Gaussian and impulsive noise.
Simulations confirm the superiority of the d-MC algorithm compared to benchmark methods.

Conclusions:

The diffusion Mixture Correntropy (d-MC) algorithm offers a robust solution for distributed estimation over dynamic graph signals.
The d-MC algorithm effectively mitigates the impact of non-Gaussian noise, enhancing estimation accuracy and stability.
This work provides a valuable advancement for signal processing in noisy, dynamic network environments.