  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.
