Are UAVs' Flight Controller Software Reliable?

Abstract

Unmanned Ariel Vehicles (UAVs) are recently being studied and worked upon to make them safe and secure for the upcoming expected growth of UAVs in civilian airspace. These efforts resulted in services such as Unmanned Aircraft System Traffic Management (UTM) or U-space in Europe, providing services to regularize and organize (pre-flight), monitor/track (during the flight) drones in civilian airspace while avoiding collisions. The primary source of information for tracking drones during flight is GPS positioning data, which is used and filtered (after being fused with the other sensors' data) by the flight controller software to estimate the vehicle position, velocity, and orientation. Extended Kalman Filter (EKF), which is used in most open-source flight controllers such as PX4, is responsible for doing this estimation. This makes EKF a critical component of the whole system. This paper aims to study the reliability of flight controllers and their core component, namely EKF, in the presence of GPS-related failures. To do so, we injected faults (i.e., we emulated failures indeed) on GPS raw data ranging from small noises to complete failure (missing GPS signals) and GPS spoofing to study their impact on EKF estimation and on the system as a whole. We observed that for small faults (e.g., Fixed Small Noise or Freeze Values), EKF is efficient and can tolerate/compensate the faults, whereas there is a gap in the filter for handling bigger anomalies (e.g., Invalid Values or Random Values) in the GPS data. Our research also clearly demonstrates that GPS faults lasting 30 seconds or more have a noticeable effect, which represents a clear vulnerability since GPS can be subject of cyber attacks such as spoofing. The quantification of the impact of GPS-related failures in the PX4 is an essential step to measure and improve the reliability of UAVs' flight controller software.