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This work develops a robust diffusion recursive least squares algorithm tomitigate the performance degradation often experienced in networks of agents inthe presence of impulsive noise. This algorithm minimizes an exponentiallyweighted least-squares cost function subject to a time-dependent constraint onthe squared norm of the intermediate estimate update at each node. With thehelp of side information, the constraint is recursively updated in a diffusionstrategy. Moreover, a control strategy for resetting the constraint is alsoproposed to retain good tracking capability when the estimated parameterssuddenly change. Simulations show the superiority of the proposed algorithmover previously reported techniques in various impulsive noise scenarios.

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