Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents
This work addresses robustness in distributed estimation for networked systems, but it appears incremental as it builds on prior diffusion methods with specific enhancements.
The paper tackles performance degradation in networked agents due to impulsive noise by developing a robust diffusion recursive least squares algorithm with side information, resulting in superior performance over existing techniques in simulations.
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.