Range and Bearing Data Fusion for Precise Convex Network Localization

TL;DR

This paper introduces a convex relaxation method for hybrid network localization using range and bearing data, significantly improving accuracy and computational efficiency over existing approaches in GNSS-challenged environments.

Contribution

It proposes a tight convex surrogate for the maximum-likelihood estimator that fuses range and bearing measurements, outperforming state-of-the-art methods in accuracy and efficiency.

Findings

Outperforms SDP relaxation by one order of magnitude in localization error

Provides a lightweight solution algorithm for hybrid localization

Characterizes the behavior of the relaxation in simulation

Abstract

Hybrid localization in GNSS-challenged environments using measured ranges and angles is becoming increasingly popular, in particular with the advent of multimodal communication systems. Here, we address the hybrid network localization problem using ranges and bearings to jointly determine the positions of a number of agents through a single maximum-likelihood (ML) optimization problem that seamlessly fuses all the available pairwise range and angle measurements. We propose a tight convex surrogate to the ML estimator, we examine practical measures for the accuracy of the relaxation, and we comprehensively characterize its behavior in simulation. We found that our relaxation outperforms a state of the art SDP relaxation by one order of magnitude in terms of localization error, and is amenable to much more lightweight solution algorithms.