Unmanned aircraft vehicles (UAVs) such as drones mostly rely on global navigation satellite systems (GNSS) to fly a proper course. It is crucial to ensure that the GNSS signal receivers of the UAVs function well before they fly, so they undergo simulation testing first. In line with this, some GNSS experts have shared some considerations when simulating GNSS signal for UAVS, including the following:
Intentional and Unintentional Interferences
Several tech head officers, including a CEO of a laboratory company for technology development in America, say that the primary challenge in GNSS simulation for UAVS is the interferences. Both intentional and unintentional interferences must be simulated to test how well a UAV receives GNSS signal amidst active radio frequency (RF) interference, for instance. Experts advise testers to use at least two RF outputs to simulate interferences efficiently.
Drastic Changes and Complex Scenarios
The simulation should evaluate the capacities of UAVs in harsh environments to make sure that the UAVs could still comply with the safety and other necessary regulations once they underwent complex scenarios. GNSS signal simulations must involve multipath signals, radical changes in satellite constellations, and inconsistent signal qualities. Even other signals — such as threats, communications, and PNT — that may be present in the environment should be tested, as well.
Realistic Flight Profiles
Though simulators should push the navigation system of the UAVs to the limits, experts remind simulation assessors to always consider the relative dynamics between the UAV and the projected environment. The simulation, after all, must always support realistic flight profiles.
Experts can’t stress enough the importance of simulation to ensure the smooth navigating performance of UAVs. But of course, there will always be challenges to the tests. So with the considerations mentioned above, they hope they help UAV users, as well as the simulator manufacturers, to maximize the GNSS signal simulation to its fullest potential.