Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents

This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent const...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4099 - 4103
Main Authors Yu, Yi, Zhao, Haiquan, de Lamare, Rodrigo C., Zakharov, Yuriy
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Adaptive algorithms
Convergence
Cost function
Diffusion cooperation
Distributed algorithms
Estimation
impulsive noise
Noise measurement
robust recursive least squares
Robustness
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ISSN2379-190X
DOI10.1109/ICASSP.2018.8462217

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Summary:This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate estimate update at each node. With the help of side information, the constraint is recursively updated in a diffusion strategy. Moreover, a control strategy for resetting the constraint is also proposed to retain good tracking capability when the estimated parameters suddenly change. Simulations show the superiority of the proposed algorithm over previously reported techniques in various impulsive noise scenarios.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462217