Cooperative Localization in Massive Networks

TL;DR

This paper analyzes the accuracy of cooperative localization in large wireless networks, revealing that average localization error increases logarithmically with decreasing anchor density, using a novel EFIM-based approach.

Contribution

It introduces a random-walk-inspired analysis of EFIM and a position information routing interpretation for large-scale cooperative network localization.

Findings

Localization error grows logarithmically with inverse anchor density.

The approach applies to large lattice and stochastic geometric networks.

Numerical examples validate the theoretical results.

Abstract

Network localization is capable of providing accurate and ubiquitous position information for numerous wireless applications. This paper studies the accuracy of cooperative network localization in large-scale wireless networks. Based on a decomposition of the equivalent Fisher information matrix (EFIM), we develop a random-walk-inspired approach for the analysis of EFIM, and propose a position information routing interpretation of cooperative network localization. Using this approach, we show that in large lattice and stochastic geometric networks, when anchors are uniformly distributed, the average localization error of agents grows logarithmically with the reciprocal of anchor density in an asymptotic regime. The results are further illustrated using numerical examples.