Exploiting Sparsity for Localization of Large-Scale Wireless Sensor Networks

TL;DR

This paper introduces the Low-Bandwidth Extended Kalman Filter (LB-EKF), a scalable method that exploits sparsity in large-scale wireless sensor networks to efficiently localize agents despite nonlinear measurement models.

Contribution

The paper presents a novel LB-EKF method that leverages sparsity and graph relabeling to reduce computational complexity in WSN localization.

Findings

LB-EKF significantly reduces computational effort.

Scalable localization demonstrated on random geometric graphs.

Theoretical and numerical validation of the approach.

Abstract

Wireless Sensor Network (WSN) localization refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing-based localization, the measurement model is a nonlinear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localization problem is known to be computationally expensive in these cases, an efficient approximation is desired. In this paper, we show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large-scale WSN localization. In the proposed method, which we call the Low-Bandwidth Extended Kalman Filter (LB-EKF), the measurement information matrix is converted into a banded matrix by relabeling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modeled as random geometric graphs) can be localized in a scalable manner using the proposed LB-EKF approach.