Analysis of GNC Systems for Class I and II UAS
The growth of the UAS (Unmanned Aerial System) market in recent years has made evident the need to classify UAS according to the specific mission requirements they must fulfill.
Common classification criteria are based on their weight and size, distinguishing between the smallest UAS (e.g., nano, micro, mini, short-range) to the largest (e.g., tactical, MALE: Medium Altitude Long Endurance, or HALE: High Altitude Long Endurance). On the other hand, according to the classification provided by the North Atlantic Treaty Organisation (NATO), the different types of UAS are associated with a specific altitude, range, and type of mission.
Class I and II UAS platforms are used for the vast majority of commercial applications, normally associated with a medium level of risk. This is thanks to their logistical simplicity and the great versatility they offer for carrying out different types of missions. The characteristic aspects of this type of platform are the GNC (Guidance, Navigation and Control) systems, since they are based on specific considerations according to the type of mission and flight envelope (for example, aerodynamic compressibility effects are neglected, use of a standard atmospheric model, etc.).
The flight control system is in charge of guiding, controlling and navigating the vehicle. This is made up of two parts: an onboard part, made up of the onboard computer (FCC: Flight Control Computer), payloads and actuators, and the ground one made up of a control station (GCS: Ground Control System), a command unit (CU) and the necessary interfaces to allow interaction with the operator on the ground.
The FCC system is considered the central system or "brain" of the Unmanned Aerial Vehicle (UAV) since it is a compact and self-contained system in charge of sending commands to the actuators connected to the control surfaces. In real-time, it also receives, sends and manages all the information from the onboard systems (peripherals systems, payloads, etc.) and the ground station (telemetry, operator-specific commands, etc.).
Its functional architecture consists of three main system groups:
- Guidance System: It is the system in charge of calculating the UAV trajectory, periodically checking the flight situation and also detecting any change or event that implies an immediate update of the flight conditions (an emergency situation, completion of the flight plan, etc.).
- Control System: It is the system in charge of calculating all the outputs of the actuators connected to the control surfaces, both to stabilise the platform against strong winds or external disturbances and to correct trajectory errors derived from the guidance system.
- Estimator: It is the system in charge of predicting the dynamic state of the vehicle, defined by the position, speed and orientation of the vehicle in space. This calculation is made using the different sensors on board, combining all the information available at any given time using sensor fusion techniques to obtain a robust and accurate estimate.
The following figure shows the relationship between the different systems mentioned above as part of the FCC system architecture:
Regarding the estimation process for small and medium-sized UAS, generally, the main sensors involved are:
IMU (Inertial Measurement Unit) / AHRS (Attitude and Heading Reference System)
The rotational state of the vehicle (angular coordinates and rate of rotation) is usually estimated using inertial measurement units (IMUs), which typically consist of a three-axis accelerometer and a three-axis gyroscope. The accelerometer measures specific forces and the gyroscope measures angular velocities, both in the vehicle's body axis reference system. They also often include magnetometers, which allow us to observe the magnetic field vector and are the main method for estimating vehicle's direction. Microelectromechanical Systems (MEMS) technology is commonly used for these sensors. They have evolved over the last few decades, allowing navigation solutions to have higher performance that are more affordable in terms of reduced size, cost, and energy consumption.
On the other hand, the AHRS is the system in charge of receiving all the information from the sensors and then executing the appropriate algorithms to estimate the attitude and heading of the platform with respect to an inertial reference system. When used for navigation, inertial sensors also provide relative positions and speeds, resulting in a complete AHRS/ Inertial Navigation System (INS). Generally the information from the inertial sensors is provided at a higher frequency and is independent from external interference. However, they are noisy and unstable in the long term.
GNSS (Global Navigation Satellite System)
The GNSS is mainly used for navigation and provides position, velocity and time information. Position, defined by longitude, latitude and altitude, and velocity are given in global coordinates. Their use is essential for an accurate estimate of the translational state of the vehicle, as they compensate for navigational drift caused by the integration of accelerations and angular speeds. In addition, the dependence on the GNSS is due also to the numerous and complex on-board electronic systems and payloads, and their precise time synchronisation is necessary for the combination of the different data collected. In this sense, the Global Navigation Satellite System has been proven to be the best alternative in the sector to establish this synchronisation.
In this case, although GNSS systems are accurate, they are vulnerable to external interference and may not meet the availability, continuity and integrity that the operation requires. For this reason, INS and GNSS systems are often integrated together to combine the complementary capabilities of both technologies.
Finally, among the different ways of combining INS and GNSS technologies, it is worth distinguishing between loosely coupled and tightly coupled integration. On the one hand, loosely coupled integration uses the PVT (Position, Velocity and Time) outputs of the INS and GNSS as inputs to the estimator. In contrast, tightly coupled integration uses the GNSS pseudoranges as input together with the PVT output of the INS, constituting a more sophisticated type of hybridization.
ADS (Air Data System)
A complete ADS comprises a static pressure sensor, a dynamic pressure sensor and an Outside Air Temperature (OAT) sensor. On the one hand, the static pressure sensor improves the vertical accuracy provided by GNSS and also provides a reliable and robust altitude estimation method, even in the absence of GNSS signals. On the other hand, the dynamic pressure sensor measures the airspeed of the UAV, which is useful for compensating the noise present in the accelerometer measurements. The OAT contributes to estimate the density of air. However, if a dedicated ADS is not available on the platform or the platform is gliding at low speed, the speed provided by the GNSS receiver, conveniently transformed to body axes, is the key reference for the estimation of the inertial forces.
Technological challenges
Safety continues to be the top priority and the driving factor for the massive irruption of UAS in multiple applications. A robust and safe operation, together with the coexistence of multiple platforms in heavily exploited airspaces, is the main technological and regulatory challenge currently being pursued.
Robust operations must consider both operational safety requirements and those that allow a resilient operation against malicious attacks.
For the former, the necessary quality assurance standards for the associated technologies must be established. In this sense, a compromise solution must be found between the levels required to guarantee effective and safe operations at a competitive cost. Fortunately, a clear trend in this direction can be observed with the new European Organisation for Civil Aviation Equipment (EUROCAE) working groups and the new European regulations, in which different categories have already been identified. Quantitative criteria are beginning to be defined to limit the compliance level according to each type of operation.
As for the latter, the systems and technologies on which UAS operations are based must be designed to operate in hostile environments where illegal interference can be a threat. For this reason, it is essential to provide systems with identification capability and the necessary robustness to mitigate these attacks and with it, investigate combined technologies that allow the intelligent fusion of numerous sources of information, avoiding single points of failure or excessive dependence on a single system. A clear example of this is the hybridization between information from GNSS receivers and other autonomous sensors onboard.
On the other hand, new algorithms that allow greater real-time autonomy and decision-making capacity of the platforms are identified as a clear need for the future. In this sense, the new advances in artificial intelligence, and computational and communication capabilities will allow onboard processing and broadcasting massive amounts of information through high-bandwidth channels for its analysis and evaluation in more advanced and powerful computers, forming a "cloud-based system". Once again, how these communications will be protected and what the compromising solution will be, are key points to address. In addition and regarding intelligent or autonomous systems, it is worth noting the ethical implications and the need for a certain level of determinism that allows for the evaluation and qualification of these systems before their operation.
Finally, safe and efficient positioning through dedicated satellite systems or other signals of opportunity from different sources, constitutes a clear evolution vector. This includes research on more efficient and advanced algorithms for signal processing and position determination, more effective authentication and encryption systems, and resilience in complex environments, such as urban canyons. It also needs hardware developments for lighter antennas and receivers with lower power consumption and better features to operate in these scenarios that are highly saturated with radio signals.
