Robust Minimum Disturbance Diffusion LMS for Distributed Estimation

• Published in: IEEE transactions on circuits and systems. II, Express briefs Vol. 68; no. 1; pp. 521 - 525
• Main Author: Zayyani, Hadi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Algorithms; Circuits and systems; Coefficients; Cost function; Diffusion; diffusion LMS; Distributed estimation; Eigenvectors; Estimation; impulsive noise; Linear systems; Mathematical analysis; Mathematical model; Matrix methods; minimum disturbance; Noise measurement; robust; Robustness (mathematics); Signal processing algorithms; Upper bounds
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2020.3004507

This brief proposes a robust distributed estimation algorithm in presence of impulsive noise. Impulsive noises are present both in the measurements and in the communication links in a sensor network. The proposed method is essentially a diffusion LMS algorithm with optimized variable coefficients in the adaptation and combination steps. The optimized coefficients are obtained based on the minimum disturbance principle. Moreover, it is shown that the optimized coefficients of the adaptation step are found by solving a linear system of equations, while the optimized coefficients of the combination step are calculated by an eigenvector of a particular matrix. Moreover, the minimum disturbances calculated theoretically and their upper bounds are derived mathematically. Simulation results show the better performance of the proposed minimum disturbance diffusion LMS algorithm over some state-of-the-art algorithms.