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Robust Minimum Disturbance Diffusion LMS for Distributed Estimation
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DOI：10.1109/TCSII.2020.3004507.png)
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Abstract
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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.
Keywords:
Estimation
Signal processing algorithms
Mathematical model
Noise measurement
Cost function
Circuits and systems
Linear systems
Distributed estimation
impulsive noise
robust
minimum disturbance
diffusion LMS
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