An incremental block LMS algorithm for distributed adaptive estimation

Recently distributed adaptive estimation algorithms have been proposed as a solution to the issue of linear estimation over distributed networks. However, as we show in this paper their performance deteriorate considerably when the links between nodes in the network are noisy. To address this problem, in this paper we propose a new distributed incremental adaptive estimation algorithm which uses block adaptive filtering in each node. By block adaptive filtering, the communications between nodes reduces to the block length times than sample data processing, which in turn decreases the effect of noisy links. The simulation results show that our proposed algorithm outperforms in steady-state estimation error than sample data processing algorithm.