3D Mobile Localization Using Distance-only Measurements

TL;DR

This paper addresses the challenge of localizing multiple UAVs in 3D space using only noisy distance measurements, proposing an SDP-based algorithm and validating it with real flight data.

Contribution

It introduces a novel SDP-based localization algorithm for UAV groups with limited sensing, applicable to GPS-denied scenarios, and provides theoretical constraints on measurement requirements.

Findings

The proposed algorithm accurately localizes UAVs with noisy data.

Minimum measurement constraints are established for successful localization.

Experimental validation confirms the algorithm's effectiveness in real flight conditions.

Abstract

For a group of cooperating UAVs, localizing each other is often a key task. This paper studies the localization problem for a group of UAVs flying in 3D space with very limited information, i.e., when noisy distance measurements are the only type of inter-agent sensing that is available, and when only one UAV knows a global coordinate basis, the others being GPS-denied. Initially for a two-agent problem, but easily generalized to some multi-agent problems, constraints are established on the minimum number of required distance measurements required to achieve the localization. The paper also proposes an algorithm based on semidefinite programming (SDP), followed by maximum likelihood estimation using a gradient descent initialized from the SDP calculation. The efficacy of the algorithm is verified with experimental noisy flight data.