The growth of the UAS (Unmanned Aerial System) market in recent years has made evident the need to classify them according to the specific mission requirements they must fulfill. A common classification criterion is based on their weight and size, distinguishing between the smallest UAS (nano, micro, mini, short range) to the largest (tactical, MALE, Medium Altitude Long Endurance, or HALE, High Altitude Long Endurance). On the other hand, and according to the classification provided by NATO, UAS are in turn associated with specific altitude, range and mission.
For a large majority of commercial applications, usually associated with a medium risk level, Class I and II UAS platforms are used, thanks to their logistical simplicity and the great versatility they offer to perform different missions. One of the characteristic aspects of these platforms are the GNC (Guidance, Navigation and Control) systems, based on specific considerations according to the type of mission and flight envelope (e.g., aerodynamic compressibility effects are neglected, use of standard atmospheric model, etc.).
The flight control system is responsible for guidance, control and navigation of the vehicle. It is composed of two parts: an on-board part, consisting of the Flight Control Computer (FCC), payloads and actuators; and a ground part, consisting of a Ground Control System (GCS), Command Unit (CU) and the necessary interfaces for operator interaction on the ground.
The FCC is considered the central system or “brain” of the UAV (Unmanned Aerial Vehicle), as it is a compact and self-contained system responsible not only for sending commands to the actuators connected to the control surfaces, but also for receiving, sending and managing in real time all the information coming from the on-board systems (peripheral systems, payloads, etc.) and the ground station (telemetry, operator specific commands, etc.).
With respect to its functional architecture, three main blocks can be identified:
- 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 (emergency situation, flight plan completion, etc.).
- Control System: It is the system in charge of calculating all the outputs of the actuators connected to the control surfaces, both to stabilize the platform against wind gusts 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 onboard sensors, combining all the information available at any given time by means of sensory fusion techniques to obtain a robust and accurate estimate.
The following figure shows the relationship between the different blocks 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 rotation rate) is usually estimated using inertial measurement units (IMU), which usually consist of an accelerometer and a three-axis gyroscope. The accelerometer measures specific forces and the gyroscope measures angular velocities, both in the vehicle body axis reference system. They also usually include magnetometers, which allow observation of the magnetic field vector and are the main method for estimating the vehicle’s heading. The technology commonly used for these sensors is MEMS (Microelectromechanical Systems) technology, which has been evolving over the last decades, enabling navigation solutions with higher performance and more affordable in terms of reduced size, cost and power consumption.
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 of the platform with respect to an inertial reference system. When used for navigation, inertial sensors also provide relative positions and velocities, resulting in a complete AHRS/INS (Inertial Navigation System). Generally, information from inertial sensors is provided at a higher frequency and is independent of external interference. However, they are noisy and unstable in the long term.
GNSS (Global Navigation Satellite System)
GNSS is mainly used for navigation and provides position, velocity and time information. Velocity and position, defined by their longitude, latitude and altitude, are given in global coordinates, and their use is essential for accurate estimation of the translational state of the vehicle, as they compensate for the navigational drift caused by the integration of accelerations and angular velocities. In addition, the reliance on GNSS is also due to the fact that onboard electronics and payloads are becoming more numerous and complex, and precise time synchronization is required for the combination of the different data collected. In this sense, the GNSS system has proven to be the best alternative in the industry to establish such synchronization.
In this case, although GNSS systems are accurate, they are vulnerable to external interference and may not meet availability, continuity and integrity of operation. 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, a distinction can be made between loosely coupled and tightly coupled integration. On the one hand, weakly 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 and is a more sophisticated type of hybridization.
ADS (Air Data System)
A complete ADS consists of a static pressure sensor, a dynamic pressure sensor and an OAT (Outside Air Temperature) sensor. On the one hand, the static pressure sensor allows to improve the vertical accuracy provided by GNSS and also provides a reliable and robust means of estimating altitude even in the absence of GNSS signals. On the other hand, the dynamic pressure sensor measures the aerodynamic velocity of the UAS and is useful to compensate for the noise present in the accelerometer due to centripetal and translational acceleration. The OAT helps to estimate air density. However, in the case where no dedicated ADS is available on the platform or the platform is gliding at low speed, the velocity provided by the GNSS receiver, suitably transformed to body axes, is the key reference for the estimation of inertial forces.
Technological challenges of UAS
Safety remains the main priority and the determining factor for the massive irruption of UAS in multiple applications. Robust and safe operation, coupled with the coexistence of multiple platforms in densely exploited airspace, is the main technological and regulatory challenge being pursued today.
Robust operations must consider both operational security requirements and those that enable resilient operation in the face of malicious attacks.
For the former, the necessary quality assurance standards must be established for the associated technologies. In this regard, a compromise must be found between the required levels to ensure efficient and safe operations at a competitive cost. Fortunately, a clear trend in this direction can be observed with the new EUROCAE working groups (European Organization for Civil Aviation Equipment) and the new European regulations, in which different categories have already been identified and quantitative criteria are beginning to be defined to delimit the level of compliance 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 a hostile environment, where illicit interference may be a threat. For this reason, it is essential to equip the systems with the identification capability and robustness to mitigate identification capability and robustness necessary to mitigate these attacks and and thus research into combined technologies that enable the intelligent fusion of numerous sources of information, avoiding single points of failure or over-reliance on a single system. A clear example of this is the hybridization between information from GNSS receivers and other autonomous on-board sensors.
On the other hand, new algorithms that allow greater autonomy and decision-making capacity of the platforms in real time are identified as a clear need for the future. In this sense, new advances in artificial intelligence and computational and communication capabilities will allow on-board processing, but also the sending of massive information through high bandwidth channels, for analysis and evaluation in more advanced and powerful computers, forming a “cloud-based system”. Again, how these communications will be protected and compromise solutions are key points to address. Furthermore, with respect to intelligent or autonomous systems, the ethical implications and the need for a certain level of determinism that allows these systems to be evaluated and qualified prior to their operation should be emphasized.
Finally, secure and efficient positioning through dedicated satellite systems or other signals of opportunity from different sources is a clear vector of evolution. This includes research into more efficient and advanced algorithms for signal processing and position determination, more effective authentication and encryption systems, resilience in complex environments such as urban canyons, but also hardware developments for lighter receivers and antennas with lower power consumption and better performance to operate in these highly radio-saturated scenarios.