UAV-Aided Positioning Systems for Ground Devices: Fundamental Limits and Algorithms

High-precision location information formulates the basis of the modern Internet of Things (IoT). However, since the navigation signals from the global navigation satellite systems (GNSSs) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required for ground devices (GDs). Due to the advantages of their flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) show significant potential in this ground localization enhancement system. In this article, we propose a UAV aided positioning (UAP) system for GDs, where the UAVs provide valuable flying Line of Sight (LoS) observations. Specifically, we first give the fundamental limits of the proposed UAP system in terms of the Cramer-Rao low bound (CRLB), where the UAVs are treated as agents with unknown positions instead of anchors. Then, we formulate a general UAP method using the nonparametric belief propagation (NBP)-based probabilistic framework, to jointly positioning UAVs and GDs simultaneously. Moreover, a two-step clustering-based solution is given to tackle the data association challenge in the multi-UAV scenarios. We also show that proper data feedback could achieve additional performance advantages without any extra measurements. The optimal multi-UAV deployment strategy is then proposed, by which the potential of the UAP system could be fully characterized. Last but not least, we verify our solutions via numerical simulations and practical experiments, which provide meaningful insights and performance evaluations to the system design and implementations.

Automated summary

This article proposes a UAV aided positioning (UAP) system that utilizes unmanned aerial vehicles to provide valuable observations for reliable and high-precision positioning of ground devices. The article presents mathematical derivations and algorithm designs to solve the problem, and validates the solutions through simulations and experiments.