Civil Airspace Research Articles

The impact of civil airspace policies on the viability of adopting autonomous unmanned aerial vehicles in last-mile applications

This study evaluates how various UAV flight policies affect energy consumption and the required charging infrastructure in last-mile parcel delivery applications. International UAV policies are reviewed and subcategorized into nine categories based on their stringency. Assuming autonomous operations of small-size quadrotor UAVs (13 min flight range), an experimentally verified energy model and demand data are used to simulate 3D trajectories of UAV missions in a digital-twin model, simulating last-mile parcel deliveries in Toronto. A novel optimization model is developed to minimize the allocated charging stations. The results show that the maximum and minimum altitude limitations increase the required charging stations by up to 52%. Minimum horizontal clearing distance increases the required charging stations by up to 75%. The results highlight increased cost per parcel delivery associated with policy strictness ranging from $0.078 (lean policies) to $0.086 (strict policies). Overall, the results highlight the need for contextual-based policy solutions.

A comprehensive security and performance assessment of UAV authentication schemes

AbstractIn the past few years, unmanned aerial vehicles (UAVs) have significantly gained attention and popularity from industry, government, and academia. With their rapid development and deployment into the civilian airspace, UAVs play an important role in different applications, including goods delivery, search‐and‐rescue, and traffic monitoring. Therefore, providing secure communication through authentication models for UAVs is necessary for a successful and reliable flight mission. To satisfy such requirements, numerous authentication mechanisms have been proposed in the literature. However, the literature lacks a comprehensive study evaluating the security and performance of these solutions. In this article, we analyze the security and performance of 27 recent UAV authentication works by considering ten different key metrics. First, in the performance analysis, we show that the majority of UAV authentication schemes are lightweight in their communication cost. However, the storage overhead or the energy consumption is not reported by many authentication studies. Then, we reveal in the security analysis the widely employed formal models (i.e., abstract description of an authentication protocol through a mathematical model), while most of the studies lack coverage of many attacks that can target UAV systems. Afterwards, we highlight the challenges that need to be addressed in order to design and implement secure and reliable UAV authentication schemes. Finally, we summarize the lessons learned on the authentication strategies for UAVs to motivate promising direction for further research.

Airborne flight time: A comparative analysis between the U.S. and China

Actual airborne time (AAT) is the time between actual wheels-off and actual wheels-on of a flight. Given the ever-growing demand for air travel and growing flight delays, understanding the behavior of AAT is increasingly important for on time performance and delay propagation. Of particular interest is the comparison on AAT in different countries with varying air route structures, air traffic management systems, weather, and geography. This paper performs the first comparative empirical analysis of AAT behavior, focusing on the U.S. and China. The focus is on how AAT is affected by origin-destination (OD) distance, the possible pressure to reduce AAT from other parts of flight operations, hub status of departure/arrival airports, enroute and terminal traffic conditions, and convective weather. Econometric models are developed to quantify the impacts of factors on AAT behavior in China and the U.S., separately. The estimation results show that in both countries AAT is highly correlated with OD distance. Flight time in China is longer than that in the U.S. given the same OD distance which indicates a low effective speed may be the result of a low aircraft speed, or due to the flight experiencing metering, rerouting, holding or vectoring in Chinese airspace. In addition, we find that a flight has limited capability to make up for pre-departure delay. Sensitivity analysis of AAT to flight length and aircraft utilization is further conducted. Given the more abundant civil airspace, flexible routing networks, and efficient air traffic flow management (ATFM) procedures, we performed a counterfactual analysis to investigate how Chinese AATs would change if they were governed by the U.S. model. We find that this would result in significant efficiency gains for the Chinese air traffic system. On average, 13 min of AAT per flight would be saved. Systemwide fuel saving would amount to 326 million gallons with CO2 emission reduction of 2.7 million tons and direct airline operating cost saving of over $1.3 billion in 2016.

Deep learning approach for investigation of temporal radio frequency signatures of drones

SummaryThe omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two‐class classification, the presence along with identification of their make using four‐class classification. And this is further extended to the determination of their flight modes using ten‐class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short‐term memory (LSTM), convolutional neural network (CNN), and CNN‐LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two‐class classification scenario, all considered classifiers perform near to idle, whereas in a four‐class classification scenario, CNN performs best, followed by AE, CNN‐LSTM, and LSTM, respectively. Moreover, in a ten‐class classification scenario, AE far outperforms CNN, followed by LSTM and CNN‐LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.

Implementation and Real-Time Validation of a European Remain Well Clear Function for Unmanned Vehicles

The full integration of Remotely Piloted unmanned vehicles into civil airspace requires first and foremost the integration of a traffic Detect and Avoid (DAA) system into the vehicle. The DAA system supports remote pilots in performing their task of remaining Well Clear from other aircraft and avoiding collisions. Several studies related to the design of a Remain Well Clear function have been performed that served as input for the development of technical standards applicable to non-European countries. In this paper, a Remain Well Clear implementation is proposed that, using the results of past international projects, fits European airspace needs and specificities and can be acceptable to both remote pilots and air traffic controllers, with only minimal impact on the standard operating procedures used for manned aircraft. The proposed Remain Well Clear software has been successfully validated through real-time simulations with pilots and controllers in the loop considering traffic encounters and mission scenarios typically found in European airspace. The achieved results highlight the appropriate situational awareness provided by the proposed RWC function and its effective support to the remote pilot in making adequate decisions in conflict solving. Real-time simulation tests showed that, in almost all cases, an RWC maneuver is successfully performed, giving the RP sufficient time to assess the conflict, coordinate with the controller, if needed, and execute the maneuver. The fundamental role of the proposed RWC function has been especially evident in uncontrolled airspace classes where the controller does not provide any separation provision. Moreover, its effectiveness has also been tested in encounters with aircraft flying under visual flight rules in controlled airspace, where the controller is not informed or has less information regarding these aircraft. The results from validation tests imply two key potential safety benefits, namely: the mitigation of performing a collision avoidance maneuver and the prevention of potential conflict while not disrupting the traffic flow with possible further consequences of generating other potentially hazardous situations.

RIS-Aided Ground-Aerial NOMA Communications: A Distributionally Robust DRL Approach

A reconfigurable intelligent surface (RIS) aided air-to-ground uplink non-orthogonal transmission framework is investigated for next generation multiple access. Occupying the same spectrum resource, unmanned aerial vehicle (UAV) users and ground users (GUs) are connected to terrestrial cellular networks via the uplink non-orthogonal multiple access (NOMA) protocol. As the flight safety is important for employing UAVs in civil airspace, the collision avoidance mechanism has to be considered during the flight. Therefore, a joint optimization problem of the UAV trajectory design, RIS configuration, and uploading power control is formulated for maximizing the network sum rate, while ensuring the UAV’s fight safety and satisfying the minimum data rate requirements of both the UAV and GU. The resultant problem is a sequential decision making one across multiple coherent time slots. Besides, the unknown locations of obstacles bring uncertainties into the decision making process. To tackle this challenging problem, a sample-efficient deep reinforcement learning (DRL) algorithm is proposed to optimize the UAV trajectory, RIS configuration, and power control simultaneously. Moreover, considering the ambiguous uncertainties in the environment, a distributionally robust DRL algorithm is further proposed to provide the worst-case performance guarantee. Numerical results demonstrate that the two proposed DRL algorithms outperform the conventional ones in terms of learning efficiency and robustness. It is also shown that the network sum rate is significantly improved by the proposed RIS-NOMA scheme compared to the conventional RIS-orthogonal multiple access (OMA) scheme and the case where no RIS is deployed.

Threat Management Methodology for Unmanned Aerial Systems Operating in the U-Space

This paper presents a threat management methodology for <i>Unmanned Aircraft Systems</i> (UAS) operating in the civil airspace. The work is framed within an <i>Unmanned Traffic Management</i> (UTM) system based on the U-space initiative.We propose a new method that focuses on providing the required automated decision-making during real-time threat management and conflict resolution, which is one of the main gaps in the current U-space ecosystem. Our method is capable of handling all commonplace UTM threats, as well as selecting optimal mitigation actions, trading off efficiency and safety. Our implementation is open-source and fully integrated in a UTM software architecture, implementing U-space services related to emergency management and tactical deconfliction. We demonstrate our methodology through a set of realistic use cases with actual UAS operating in civil airspace. For that, we performed field experiments in an aerodrome with segregated airspace, and we showcased that the methodology is capable of autonomously managing heterogeneous threats in real time.

SORA Methodology for Multi-UAS Airframe Inspections in an Airport

Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations requires demonstrating compliance with applicable regulations and a comprehensive understanding of the residual risk associated with the UAS operation. To support these activities and enable the safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has established a UAS regulatory framework that mandates the execution of safety risk assessment for UAS operations in order to gain authorization to carry out certain types of operations. Driven by this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment for UAS operations. However, existing work on SORA and its applications focuses mainly on single UAS operations, offering limited support for assuring operations conducted with multiple UAS and with autonomous features. Therefore, the work presented in this paper analyzes the application of SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS with autonomous features inside an airport. We present the decision-making process of each SORA step and its application to a multiple UAS scenario. The results shows that the procedures and safety features included in the Multi-AFI operation such as workspace segmentation, the independent multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can be conducted safely and can receive a positive evaluation from the competent authorities. We also present our key findings from the application of SORA and discuss how it can be extended to better support multi-UAS operations.

Control Protocol Design and Analysis for Unmanned Aircraft System Traffic Management

Due to the rapid development of technologies for small unmanned aircraft systems (sUAS’s), the supply and demand market for sUAS’s is expanding globally. With the great number of sUAS’s ready to fly in civilian airspace, an sUAS aircraft traffic management system that can guarantee the safe and efficient operation of sUAS’s is still absent. In this paper, we propose a control protocol design and analysis method for sUAS traffic management (UTM) which can safely manage a large number of sUAS’s. The benefits of our approach are two-fold: at management level, the effort for monitoring sUAS traffic (authorities) and control/planning for each sUAS (operator/pilot) are both greatly reduced under our framework; and at operational level, the behavior of individual sUAS is guaranteed to follow the restrictions. Mathematical proofs and numerical simulations are presented to demonstrate the proposed method.

A machine learning-based methodology for computational aeroacoustics predictions of multi-propeller drones

The rapid progress in technological developments of small Unmanned Aircraft Systems (sUAS) or simply "drones" has produced a significant proliferation of this technology. From multinational businesses to drone enthusiasts, such a technology can offer a wide range of possibilities, i.e., commercial services, security, and environmental applications, while placing new demands in the already-congested civil airspace. Noise emission is a key factor that is being addressed with high-fidelity computational fluid dynamics (CFD) and aeroacoustics (CAA) techniques. However, due to uncertainties of flow conditions, wide ranges of propellers' speed variations, and different payload requirements, a complete numerical prediction varying such parameters is unfeasible. In this study, a machine learning-based approach is proposed in combination with high-fidelity CFD and CAA techniques to predict drone noise emission given a wide variation of payloads or propellers' speeds. The transient CFD computations are calculated using a time-marching LES simulation with a WALE sub-grid scale. In contrast, the acoustic propagation is predicted using a finite element method in the frequency domain. Finally, the machine learning strategy is presented in the context of fulfilling two goals: (i) real-time noise prediction of drone systems; and (ii) determination of propeller's rotation speeds leading to a noise prediction matching experimental data.

Assessment of the Emerging Landscape of Unmanned Aerial Systems in Trinidad and Tobago

Interest in the civilian applications of Unmanned Aerial Systems (UAS) has been growing worldwide, especially in government and commercial tasks such as surveillance, search and rescue, inspection of infrastructure, agriculture, mining, and mapping. Likewise, Trinidad and Tobago (T&amp;T) has been witnessing a growing interest and application of commercial and non-commercial UAS operations. However, there is little assessment for the growth of the UAS market nor is there characterisation of UAS-based activities since the pertinent regulations established in 2016. This study seeks to formally identify the emerging UAS landscape in T&amp;T during the period 2015 to 2019. As such, this study maps and characterises the spatial and temporal patterns of UAS distribution, then appraises the various categories for the existing operations. To achieve these goals, this study utilised qualitative and quantitative techniques of Geoinformatics. The intent for this study is to provide a perspective on the growth and the implications of the UAS industry in T&amp;T, and to guide strategic planning among organisations with a stake in the emergence of UAS into civil airspace.

Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services

This paper presents a software architecture for Unmanned aerial system Traffic Management (UTM). The work is framed within the U-space ecosystem, which is the European initiative for UTM in the civil airspace. We propose a system that focuses on providing the required services for automated decision-making during real-time threat management and conflict resolution, which is the main gap in current UTM solutions. Nonetheless, our software architecture follows an open-source design that is modular and flexible enough to accommodate additional U-space services in future developments. In its current implementation, our UTM solution is capable of tracking the aerial operations and monitoring the airspace in real time, in order to perform in-flight emergency management and tactical deconfliction. We show experimental results in order to demonstrate the UTM system working in a realistic simulation setup. For that, we performed our tests with the UTM system and the operators of the aerial aircraft located at remote locations with the consequent communication issues, and we showcased that the system was capable of managing in real time the conflicting events in two different use cases.

Towards a framework for certification of reliable autonomous systems

A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article.

Simulation studies of a vision intruder detection system

PurposeThe purpose of this paper is to describe simulation research carried out for the needs of multi-sensor anti-collision system for light aircraft and unmanned aerial vehicles.Design/methodology/approachThis paper presents an analysis related to the practical possibilities of detecting intruders in the air space with the use of optoelectronic sensors. The theoretical part determines the influence of the angle of view, distance from the intruder and the resolution of the camera on the ability to detect objects with different linear dimensions. It has been assumed that the detection will be effective for objects represented by at least four pixels (arranged in a line) on the sensor matrix. In the main part devoted to simulation studies, the theoretical data was compared to the obtained intruders’ images. The verified simulation environment was then applied to the image processing algorithms developed for the anti-collision system.FindingsA simulation environment was obtained enabling reliable tests of the anti-collision system using optoelectronic sensors.Practical implicationsThe integration of unmanned aircraft operations in civil airspace is a serious problem on a global scale. Equipping aircraft with autonomous anti-collision systems can help solve key problems. The use of simulation techniques in the process of testing anti-collision systems allows the implementation of test scenarios that may be burdened with too much risk in real flights.Social implicationsThis paper aims for possible improvement of safety in light-sport aviation.Originality/valueThis paper conducts verification of classic flight simulator software suitability for carrying out anti-collision systems tests and development of a flight simulator platform dedicated to such tests.

Visual Flight Rules-Based Collision Avoidance Systems for UAV Flying in Civil Aerospace

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft.

Robust Optimization Models for Flight Rerouting

To solve the rerouting problem resulted from uncertain adverse weather and multiple flight-confined areas, absolute robust, deviation robust, and relative robust optimization models for flight rerouting were proposed. Maximum flow method was applied for determining the value of maximum flow for the en-route network affected by adverse weather. A numerical test based on the simulated data of a civil airspace unit was performed. Test results show that, compared with the stochastic model under equal probability scenarios, the robust optimization models gain better effect in adapting to scenario changes. The superiority of the models was verified.

System of Systems (SoS) Analysis of the Problem of UAS Integration into Controlled Airspace

AbstractUnmanned aircraft system (UAS) operations are growing in numbers, type of applications, technical complexity and regulatory challenges. National aviation regulatory authorities are engaged in coming up with regulatory and policy framework for seamless integration and operation of UAS in civil airspace. Integration of UAS into civil airspace is a complex and challenging System of Systems (SoS) engineering problem involving multiple stakeholders, complex airborne and ground systems / subsystems, processes, regulatory frameworks, etc. Recent research and developments in SoS engineering provide tools for representing, modelling and analysis of complex SoSs which would help in taking design decisions and risk mitigation. In this paper, the problem of integrating UAS into controlled civil airspace is analyzed from the SoS engineering stand point. The key system elements involved in the problem and their relationship and interactions are modelled as a SoS. The relationship between the technical challenges associated with the key SoS elements and the standard SoS dimensional properties is established. An approach for developing an architecture model of the proposed SoS is discussed.

Learning reciprocal actions for cooperative collision avoidance in quadrotor unmanned aerial vehicles

The ability to avoid collisions with each other is one of the fundamental requirements for autonomous unmanned aerial vehicles (UAVs) to be safely integrated into the civilian airspace, and for the viability of multi-UAV operations. This paper introduces a new approach for online cooperative collision avoidance between quadcopters, involving reciprocal maneuvers, i.e., coherent maneuvers without requiring any real-time consensus. Two maneuver strategies are presented, where UAVs respectively change their speed or heading to avoid a collision. A learning-based framework that trains these reciprocal actions for collision evasion (called TRACE) is developed. The primary elements of this framework include: 1) designing simulated experiments that cover a variety of UAV–UAV approach scenarios; 2) performing optimization to identify speed/heading change actions that satisfy safety constraints while minimizing the energy cost of the maneuver; and 3) using the offline optimization outcomes to train classifier (via ensemble bagged tree) and function approximation (via neural networks and Kriging) models for respectively selecting and encoding the avoidance actions. Trajectory generation and dynamics/controls are incorporated in the simulation environment used for training and testing. Over 90% accuracy in action prediction and over 95% success in avoiding collisions is observed when the trained models are applied to simulated unseen test scenarios.

Evaluation of Radar Cross Section in Aerial Operations by using POFACETS for a standard Unmanned Aerial Vehicle (UAV)

In military and common applications, radar assumes an exceptionally noteworthy job in exploring the vehicle, recognizing for flying machines, ships and so on. Radar cross section is a measure of a target's ability to reflect radar signals in the direction of the receiver. It is obvious that RCS depends on the shape of the reflecting objects. In this paper, a brief overview of various radars used in surveillance applications and radar cross section (RCS) measurement techniques are presented. The objective of this paper is to illustrate methods for obtaining the Radar Cross Section (RCS) of a typical Unmanned Aerial Vehicle (UAV) for various frequencies in radar bands. UAV technologies have advanced tremendously and are being developed successfully. These features lead UAV to patrol even in civil airspace for civilian applications. These objects operate with small radar cross-section and/or on low altitude which imposes security threat. Keeping this view the RCS of a UAV which are flying in civilian airspace has to be determined drastically for security purposes. Several shapes are considered for the estimation of RCS. The results on the variations of RCS for the above objects as a function of frequency, aspect angle are presented. The POFACETS plays a crucial role and provides the user an easy–to–use GUI that allows the input of all necessary parameters, while preventing erroneous data input and other user errors. In this paper the RCS of sphere, ellipsoid and UAV are determined.

A Medium-Term Conflict Detection and Resolution Method for Open Low-Altitude City Airspace Based on Temporally and Spatially Integrated Strategies

The balancing of civil airspace capacities with customer demands is achieved through air traffic management (ATM), which uses decision support tools in ATM systems that are ensured by advanced communications and contemporary navigation technologies. However, uncertainties and perturbations can emerge during low-altitude airspace traffic management when considering plans to gradually open up low-altitude airspace in China. Domino effects of time and space deviations to one trajectory may lead to the generation of other 4-D trajectories (4-D trajectories defined based on a series of sequential waypoints recording three dimensions of spatial information and their relevant time information) due to strong levels of spatiotemporal connectivity between trajectories, rendering management challenging. This paper introduces background information and a literature review about conflict detection and resolution (CD&R) for open low-altitude airspace. From our analysis, a CD algorithm for a spatial grid partition system (SGPS) is presented. A conflict resolution (CR) framework of temporally and spatially integrated strategies including the time scheduling-based technique and vertical change-based technique (VCBT) was applied to the CR process. The CD&R algorithm was tested based on a practical maneuver scenario to check its validity, and an analysis of computational performance levels was conducted through several experiments. Airspace capacities can be enhanced to alleviate local airspace network perturbations through the use of the proposed CD&R algorithm.