Distributed Estimation with Partially Accessible Information: An IMAT Approach to LMS Diffusion

TL;DR

This paper introduces an IMAT-based framework for distributed LMS diffusion algorithms that effectively handle partial observability by identifying support vectors, improving robustness in sparse observation scenarios.

Contribution

It proposes a novel thresholding-based algorithm inspired by signal flow analysis to address limited information in distributed estimation tasks.

Findings

Effective support vector identification in sparse data scenarios

Improved robustness of diffusion LMS algorithms with partial information

Demonstrated success in time and transform domain applications

Abstract

Distributed algorithms, particularly Diffusion Least Mean Square, are widely favored for their reliability, robustness, and fast convergence in various industries. However, limited observability of the target can compromise the integrity of the algorithm. To address this issue, this paper proposes a framework for analyzing combination strategies by drawing inspiration from signal flow analysis. A thresholding-based algorithm is also presented to identify and utilize the support vector in scenarios with missing information about the target vector's support. The proposed approach is demonstrated in two combination scenarios, showcasing the effectiveness of the algorithm in situations characterized by sparse observations in the time and transform domains.