Weighted diffusion LMP algorithm for distributed estimation in non-uniform noise conditions

TL;DR

This paper introduces a weighted diffusion LMP algorithm that improves distributed estimation accuracy in sensor networks with non-uniform noise by adaptively weighting local errors.

Contribution

It proposes a novel weighted cost function and an adaptive weight update method for diffusion LMP, enhancing performance under non-uniform noise conditions.

Findings

Outperforms standard diffusion LMP in non-uniform noise scenarios

Adaptive weighting improves estimation accuracy

Simulation confirms robustness in sensor networks

Abstract

This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated by a simple steepest-descent recursion to minimize the error signal of the global and local adaptive algorithm. Simulation results show the advantages of the proposed weighted diffusion LMP over the diffusion LMP algorithm specially in the non-uniform noise conditions in a sensor network.