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

## TL;DR

This paper introduces a robust diffusion recursive least squares algorithm for networked agents that effectively handles impulsive noise by incorporating side information and adaptive constraints, improving tracking and estimation accuracy.

## Contribution

It proposes a novel RLS algorithm with a time-dependent constraint and side information, enhancing robustness and tracking in impulsive noise environments.

## Key findings

- Outperforms existing methods in impulsive noise scenarios
- Effective constraint resetting improves tracking during parameter changes
- Demonstrates superior estimation accuracy through simulations

## 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.