Uncertainty-Aware 3D Position Refinement for Multi-UAV Systems

TL;DR

This paper introduces a decentralized 3D position refinement method for multi-UAV systems that enhances robustness and accuracy by fusing local estimates with neighbor data, accounting for uncertainties and malicious nodes.

Contribution

A novel uncertainty-aware fusion framework that handles cold start, localization loss, and malicious participants to improve multi-UAV localization robustness.

Findings

Significantly reduces localization error during cold start.

Maintains lower error levels with increasing malicious nodes.

Performs well in simulation with 10 UAVs in 3D environments.

Abstract

Reliable real-time 3D localization is essential for multi-UAV navigation, collision avoidance, and coordinated flight, yet onboard estimates can degrade under GNSS multipath, non-line-of-sight reception, vertical drift, and intentional interference. This paper presents a decentralized, lightweight 3D position-refinement layer that improves robustness by fusing each Unmanned Aerial Vehicle (UAV)'s local estimate with neighbor-shared state summaries and inter-UAV range or proximity constraints. The method performs uncertainty-aware neighborhood fusion by weighting each UAV's prior according to its reported covariance and weighting neighbor constraints according to link quality, ranging uncertainty, and a learned trust score. To support practical deployment, the framework explicitly handles cold start and temporary localization loss by inflating or substituting weak priors, allowing trusted neighborhood constraints to bootstrap and stabilize estimates until absolute sensing recovers. To mitigate the impact of faulty or malicious participants, each UAV applies a local range-consistency check, smoothed over time, to down-weight or exclude neighbors whose reported positions are incompatible with observed inter-UAV distances. Simulation experiments with 10 UAVs in a 3D volume show that the proposed refinement substantially reduces mean localization error during cold start, remains competitive after local estimators stabilize, and maintains lower error as the fraction of malicious nodes increases compared with fusion without trust. These results suggest that the approach can serve as a practical resilience layer for swarm operation in challenging environments.