Networked UAV Detection and Alerting of Ionospheric Anomalies within LADGNSS Navigation Framework

Abstract

Local Area Differential GNSS (LADGNSS) is a simplification of the Ground-based Augmentation System (GBAS) architecture to provide navigation and guidance for nearby UAVs. Recent research has evaluated the impacts of severe ionospheric anomalies on Dual-Frequency, Multiple-Constellation (DFDC) LADGNSS and its monitors. To protect against all possible ionospheric anomalies, the system assumes that the worst-case undetected ionosphere-induced differential errors might affect users. The hypothetical impact of ionospheric anomalies can be lowered by improving monitoring capability, and one possible improvement not studied previously is to utilize two-way transmissions from the multiple networked UAVs supported by LADGNSS at any given time. Two-way datalinks are already part of the LADGNSS architecture to support UAV status reports and cooperative guidance, and they can be exploited to allow all UAVs connected to LADGNSS ground stations to share the threat information collected by any of them. This paper examines the benefit of networked UAVs for LADGNSS under anomalous ionospheric conditions by proposing a monitor strategy that leverages observations from networked UAVs to enhance ionospheric monitor capability. The strategy utilizes UAVs within the network which have superior ionospheric gradient monitor (IGM) detection capability to reduce the maximum undetected gradient of a specific user UAV, leading to reduced maximum undetected ionosphere-induced differential errors (MIEV) at that user. Simulation results show a significant reduction in the worst ionosphere-induced differential error by utilizing multiple monitor capability of networked UAVs, where the benefit increases with a larger number of UAVs that are more widely distributed.