Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents

Abstract

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.

6 pages, 4 figures

References in corpus (4)

• Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
• Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation over Adaptive Networks
• Distributed Pareto Optimization via Diffusion Strategies
• Diffusion Least Mean P-Power Algorithms for Distributed Estimation in Alpha-Stable Noise Environments

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