High-Integrity Local-Area Differential GNSS Architectures Optimized to Support Unmanned Aerial Vehicles (UAVs)

As the applications of Unmanned Aerial Vehicles (UAVs) expand, UAVs will be combined into networks that cooperate to perform various missions within 10 to 200 km of a centralized controller. GNSS is the primary source of navigation for UAVs operating over large areas, and UAVs combined into local networks can easily make use of local-area differential corrections integrated into their guidance commands to improve their navigation accuracy and integrity. This paper develops a Local-Area Differential GNSS (LADGNSS) architecture around a concept of local-area UAV network operations that emphasizes low cost for commercial applications and high integrity to allow UAVs to operate in close proximity to each other and potential "targets" while minimizing collision risk. Using the well-established Ground-based Augmentation System (GBAS) as a starting point, a simplified LADGNSS architecture is identified that retains most of the performance of GBAS at a far lower cost. Because LADGNSS performance will be limited by the characteristics of UAV receivers and flight dynamics, future work will be focused on identifying and understanding UAV receiver performance through a series of flight tests at the Korea Advanced Institute of Science and Technology (KAIST). 1.0 Introduction: UAV Network Concept While the best-known applications of Unmanned Aerial Vehicles (UAVs) are remotely-piloted military reconnaissance and strikes using relatively large aircraft, commercial applications of much smaller UAVs have grown dramatically over the past few years and are now of major interest to the media (see [1]). A large number of applications have been proposed, and many of these have already been put into practice in certain places due to the capability and inexpensiveness of today's UAV and controller hardware [2]. This emerging reality should also make networks of UAVs guided by a single intelligence (either human or artificial) practical, if not now, within the next few years. The applications proposed for UAV networks can be divided into two categories. The first is observation and data collection, where the objective is to measure or monitor something that changes relatively slowly but is difficult or costly to observe by other methods. Aerial photography is one example that is already popular, as UAVs can perform this function much more cheaply than manned aircraft. Near-real-time observations of Arctic ice are another potential application, as the growth of shipping in the Arctic will likely require more detailed and more frequent observations of ice than can be made by satellites. A more unusual application proposed by Prof. Grace Gao of the University of Illinois is monitoring the ejecta of volcanoes to assess the resulting environmental hazards. The eruption of the volcano Eyjafjallajökull in Iceland in 2010 showed the usefulness of such monitoring, as the resulting clouds of ash posed a potential hazard to aviation and caused passenger flights in and around Western Europe to be suspended intermittently over several weeks [3]. This was very disruptive to people and business but was necessary due to the high level of uncertainty regarding the level of danger posed by the ash cloud in various locations. The second category of UAV network applications is reconnaissance and surveillance. It shares with the first category the general motivation of collecting information, but the key difference is the need to detect and react to anomalies quickly. Military needs for reconnaissance and surveillance are widespread and, to some degree, are being carried out by the existing array of military UAVs. However, a networked approach that is mostly (if not completely) automated would take much of the burden off soldiers who have to operate and coordinate today's UAVs. Many facilities in the civil world share the need for all-the-time monitoring and could benefit from this Figure 1: Local-Area UAV Network Concept technology. If it is sufficiently inexpensive, the market could grow from obvious candidates like airports, prisons, shopping malls, and company/university campuses to residential complexes and neighborhoods. Figure 1 illustrates one concept of local-area UAV network operations [4,5]. The control station shown at the lower left is the source of LADGNSS corrections and integrity information as well as real-time guidance for each UAV in the network. The LADGNSS and guidance information are separate data messages combined in the same outbound transmission. Because the guidance function requires feedback from each UAV, the datalink is two-way and can be used to relay GNSS information as well from UAVs to the control station, although signals from UAVs are at a lower update rate. Most of the time, individual UAVs are “on station,” meaning that they are stationary or nearly so and are observing the ground, taking measurements or photographs, etc. Because the endurance of each UAV is limited, the control station must recover, refuel, and re-launch UAVs periodically at a site near the control station. Specific pathways in space are defined to separate deploying and returning UAVs from those on station. All UAVs must maintain safe separation from each other, from other (non-participating) UAVs, from the ground and obstructions on the ground, and from manned aircraft. The primary responsibility of each UAV is to stabilize itself and to control its motion from one location to another as guided by the central controller. Section 2.0 of this paper describes simple, commercialoff-the-shelf (COTS) LADGNSS systems as well as the very complex and robust GBAS architecture as starting points for the design of an LADGNSS approach most suited for the operation concept shown in Figure 1. Section 3.0 uses GBAS as the starting point and explains how the GBAS ground system can be simplified for this application to remove the most complex and expensive components of GBAS while retaining the performance of GBAS that is feasible in the context of UAV navigation. Section 4.0 describes how the information in the groundto-airborne datalink can be simplified. Section 5.0 explains the modifications on the airborne (UAV receiver) side and how selected information is relayed back from each UAV to the ground system. Section 6.0 describes the future work needed to fully develop this concept, in particular, the need for UAV flight tests to better quantify the performance of UAV receivers as part of LADGNSS. Section 7.0 briefly summarizes the paper. 2.0 Local-Area DGNSS Architecture Alternatives 2.1 Commercial DGPS Used in Testing Figure 2 shows both the ground (reference receiver) and mobile (UAV) hardware for the dual-frequency NovAtel LADGPS system used by the Unmanned System Research Group at KAIST for UAV flight testing UAVs [6]. This system provides L1/L2 code and carrier differential corrections to support RTK as well as codeUpper buffer (bhigh)