Spatial Whitening Framework for Distributed Estimation

TL;DR

This paper introduces a spatial whitening framework for distributed estimation in sensor networks, improving resource allocation by reducing data correlation through neighbor exchange, with an iterative scheme for large networks.

Contribution

It proposes an adjacency-based spatial whitening scheme with an iterative optimization method, addressing computational challenges and enhancing distributed estimation performance.

Findings

Effective whitening improves estimation accuracy.

Iterative scheme scales to large networks.

Framework outperforms traditional methods.

Abstract

Designing resource allocation strategies for power constrained sensor network in the presence of correlated data often gives rise to intractable problem formulations. In such situations, applying well-known strategies derived from conditional-independence assumption may turn out to be fairly suboptimal. In this paper, we address this issue by proposing an adjacency-based spatial whitening scheme, where each sensor exchanges its observation with their neighbors prior to encoding their own private information and transmitting it to the fusion center. We comment on the computational limitations for obtaining the optimal whitening transformation, and propose an iterative optimization scheme to achieve the same for large networks. We demonstrate the efficacy of the whitening framework by considering the example of bit-allocation for distributed estimation.