Browsing by Author "Panagiotakopoulos, Dimitrios"
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Item Open Access Aspects and challenges of unmanned aircraft systems safety assurance and certification for advanced operations(AIAA, 2021-07-28) Karyotakis, Michail K.; Panagiotakopoulos, Dimitrios; Braithwaite, Graham; Tsourdos, AntoniosLike manned aviation, a Safety Management System (SMS) needs to be developed for Unmanned Aircraft Systems (UAS) taking into account their unique characteristics, and the huge variety of different operations they can perform. Towards developing a SMS for unmanned aviation this paper focuses on Safety Assurance and Certification for advanced UAS operations. Based on the manned aviation practices and the Concepts of Operations (ConOps) that have been developed for UAS, this paper examines two indicative operational scenarios (OS) in unmanned aviation identifying the gaps in the view of the safety assurance and certification processes. The findings form the basis for the proposal and development of a new safety management framework for certain UAS operations. The examination of OS shows that operation-centric operational approvals as well as faster integration of UAS to the current airspace may be possible under certain conditions.Item Open Access Deep learning architecture for UAV traffic-density prediction(MDPI, 2023-01-22) Alharbi, Abdulrahman; Petrunin, Ivan; Panagiotakopoulos, DimitriosThe research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average.Item Open Access A deep mixture of experts network for drone trajectory intent classification and prediction using non-cooperative radar data(IEEE, 2024-01-01) Fraser, Benjamin; Perrusquía, Adolfo; Panagiotakopoulos, Dimitrios; Guo, WeisiThe intent prediction of unmanned aerial vehicles (UAVs) also known as drones is a challenging task due to the different mission profiles and tasks that the drone can perform. To alleviate this issue, this paper proposes a deep mixture of experts network to classify and predict drones trajectories measured from non-cooperative radars. Telemetry data of open-access datasets are converted to simulated radar tracks to generate a pool of heterogeneous trajectories and construct three independent datasets to train, validate, and test the proposed architecture. The network is composed of two main components: i) a deep network that predicts the class associated to the input trajectories and ii) a set of deep experts models that learns the extreme bounds of the trajectories in different future time steps. The proposed approach is tested and compared with different deep models to verify its effectiveness under different flight profiles and time-windows.Item Open Access Developing drone experimentation facility: progress, challenges and cUAS consideration(IEEE, 2021-07-02) Panagiotakopoulos, Dimitrios; Williamson, Alex; Petrunin, Ivan; Harman, Stephen; Quilter, Tim; Williams-Wynn, Ian; Goudie, Gavin; Watson, Neil; Vernall, Phil; Reid, Jonathan; Puscius, Eimantas; Cole, Adrian; Tsourdos, AntoniosThe operation of Unmanned Aerial Systems (UAS) is widely recognised to be limited globally by challenges associated with gaining regulatory approval for flight Beyond Visual Line of Sight (BVLOS) from the UAS Remote Pilot. This challenge extends from unmanned aircraft flights having to follow the same ‘see and avoid’ regulatory principles with respect to collision avoidance as for manned aircraft. Due to the technical challenges of UAS and Remote Pilots being adequately informed of potential traffic threats, this requirement effectively prohibits BVLOS UAS flight in uncontrolled airspace, unless a specific UAS operational airspace is segregated from manned aviation traffic, often achieved by use of a Temporary Danger Area (TDA) or other spatial arrangements. The UK Civilian Aviation Authority (CAA) has defined a Detect and Avoid (DAA) framework for operators of UAS to follow in order to demonstrate effective collision avoidance capability, and hence the ability to satisfy the ‘see and avoid’ requirement. The National BVLOS Experimentation Corridor (NBEC) is an initiative to create a drone experimentation facility that incorporates a range of surveillance and navigation information sources, including radars, data fusion, and operational procedures in order to demonstrate a capable DAA System. The NBEC is part located within an active Airodrome Traffic Zone (ATZ) at Cranfield Airport, which further creates the opportunity to develop and test systems and procedures together with an operational Air Traffic Control (ATC) unit. This allows for manned and unmanned traffic to be integrated from both systems and procedural perspectives inside segregated airspace in a first stage, and then subsequently transiting to/from non-segregated airspace. The NBEC provides the environment in which a number of challenges can be addressed. This paper discusses the lack of target performance parameters, the methodology for gaining regulatory approval for non-segregated BVLOS flights and for defining peformance parameters for counter UAS (cUAS).Item Open Access Digital twin development for the airspace of the future(MDPI, 2023-07-23) Souanef, Toufik; Al-Rubaye, Saba; Tsourdos, Antonios; Ayo, Samuel; Panagiotakopoulos, DimitriosThe UK aviation industry is committed to achieving net zero emissions by 2050 through sustainable measures and one of the key aspects of this effort is the implementation of Unmanned Traffic Management (UTM) systems. These UTM systems play a crucial role in enabling the safe and efficient integration of unmanned aerial vehicles (UAVs) into the airspace. As part of the Airspace of the Future (AoF) project, the development and implementation of UTM services have been prioritised. This paper aims to create an environment where routine drone services can operate safely and effectively. To facilitate this, a digital twin of the National Beyond Visual Line of Sight Experimentation Corridor has been created. This digital twin serves as a virtual replica of the corridor and allows for the synthetic testing of unmanned traffic management concepts. The implementation of the digital twin involves both simulated and hybrid flights with real drones. Simulated flights allow for the testing and refinement of UTM services in a controlled environment. Hybrid flights, on the other hand, involve the integration of real drones into the airspace to assess their performance and compatibility with the UTM systems. By leveraging the capabilities of UTM systems and utilising the digital twin for testing, the AoF project aims to advance the development of safer and more efficient drone operations. The Experimentation Corridor has been developed to simulate and test concepts related to managing unmanned traffic. The paper provides a detailed account of the implementation of the digital twin for the AoF project, including simulated and hybrid flights involving real drones.Item Open Access An explainable artificial intelligence (xAI) framework for improving trust in automated ATM tools(IEEE, 2021-11-15) Sanchez Hernandez, Carolina; Ayo, Samuel; Panagiotakopoulos, DimitriosWith the increased use of intelligent Decision Support Tools in Air Traffic Management (ATM) and inclusion of non-traditional entities, regulators and end users need assurance that new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are trustworthy and safe. Although there is a wide amount of research on the technologies themselves, there seem to be a gap between research projects and practical implementation due to different regulatory and practical challenges including the need for transparency and explainability of solutions. In order to help address these challenges, a novel framework to enable trust on AI-based automated solutions is presented based on current guidelines and end user feedback. Finally, recommendations are provided to bridge the gap between research and implementation of AI and ML-based solutions using our framework as a mechanism to aid advances of AI technology within ATM.Item Open Access Hybrid deep neural networks for drone high level intent classification using non-cooperative radar data(IEEE, 2023-09-22) Fraser, Benjamin; Perrusquía, Adolfo; Panagiotakopoulos, Dimitrios; Guo, WeisiThe proliferation of drones has brought many benefits in different industrial and government sectors due to their low cost and potential applications. Nevertheless, the security and air space can be compromised due to anomalous performances derived to negligence or intentional malicious activities. Thus, identify the hidden intentions of drones’ mission profiles is paramount to execute adequate countermeasures. In this paper, an hybrid deep neural network architecture is proposed to classify the high level intent of drones’ mission profiles using non-cooperative radar. Radar measurements are created synthetically using open access telemetry data of flight trajectories. The proposed architecture exploits the classification and reconstruction capabilities of deep neural models to classify the drones hidden high-level intent. Several experiments and comparisons are carried out to verify the effectiveness of the proposed approach.Item Open Access Identification and characterization of traffic flow patterns for UTM application(IEEE, 2021-11-15) Alharbi, Abdulrahman; Petrunin, Ivan; Panagiotakopoulos, DimitriosThe current airspace has limited resource, and the widespread use of Unmanned Aircraft System (UAS) is increasing the density of civilian aircraft that is already crowded with manned aerial vehicles. This increased density in airspace demands to improve the safety, efficiency and capacity of airspace while considering all uncertain parameters that may cause hinderance in aircraft movement like weather and dynamic fluctuations. A systematic analysis of correlations between events and their impacts in air traffic network is a considerable challenge. This paper proposes a methodology that characterizes and identifies the patterns of Unmanned Traffic Management (UTM) airspace based on the analysis of simulated data to improve the performance of UTM network as well as ensuring its safety and capacity. Some sets of metrics are defined to identify the airspace characteristics that include airspace density, capacity and efficiency. The data analysis carried out here, will support risk analysis and improve trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather conditions, loss of safe distances, UAVs’ performance, emergency services and airspace structures that may cause deviations from their standard paths.Item Open Access Interference mitigation for 5G-connected UAV using deep Q-learning framework(IEEE, 2022-10-31) Warrier, Anirudh; Al-Rubaye, Saba; Panagiotakopoulos, Dimitrios; Inalhan, Gokhan; Tsourdos, AntoniosTo boost large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm namely cellular-connected UAVs has recently received an upsurge of interest in both academia and industry. Fifth generation (5G) networks are expected to support this large-scale deployment with high reliability and low latency. Due to the high mobility, speed, and altitude of the UAVs there are numerous challenges that hinder its integration with the 5G architecture. Interference is one of the major roadblocks to ensuring the efficient co-existence between UAVs and terrestrial users in 5G networks. Conventional interference mitigation schemes for terrestrial networks are insufficient to deal with the more severe air-ground interference, which thus motivates this paper to propose a new algorithm to mitigate interference. A deep Q-learning (DQL) based algorithm is developed to mitigate interference intelligently through power control. The proposed algorithm formulates a non-convex optimization problem to maximize the Signal to Interference and Noise Ratio (SINR) and solves it using DQL. Its performance is measured as effective SINR against the complement cumulative distribution function. Further, it is compared with an adaptive link technique: Fixed Power Allocation (FPA), a standard power control scheme and tabular Q-learning(TQL). It is seen that the FPA has the worst performance while the TQL performs slightly better. This is since power control and interference coordination are introduced but not as effectively in the TQL method. It is observed that DQL algorithm outperforms the TQL implementation. To solve the severe air-ground interference experienced by the UAVs in 5G networks, this paper proposes a DQL algorithm. The algorithm effectively mitigates interference by optimizing SINR of the air-ground link and outperforms the existing methods. This paper therefore, proposes an effective algorithm to resolve the interference challenge in air-ground links for 5G-connected UAVs.Item Open Access The legal framework of UTM for UAS(IEEE, 2020-11-18) Ryan, Richard; Al-Rubaye, Saba; Braithwaite, Graham; Panagiotakopoulos, DimitriosIt is very apparent that the legal framework for Unmanned aircraft system Traffic Management (UTM) needs to be developed as regulators grapple with issues that relate to legal responsibility and accountability for each UTM stakeholder as the proliferation of drones increases. There is a considerable ‘legal lacuna’ that exists creating much uncertainty within the industry with respect to investment and the direction of innovation. Drones are being utilised today under controlled conditions as technology and ability develops, but with this accelerated pace of technological development, existing regulations soon become limited to address new capabilities and thus become out of date. Policy has become law in many jurisdictions, but policy needs to be developed further to keep pace with demand because safety is paramount. This paper investigates and highlights legal aspects that a regulator and UTM stakeholders have to consider in developing good drone law. It is essential that a properly considered legal framework is developed for many reasons including, but not limited to, increased positive public perception, proliferation of innovation of use cases for Unmanned Aerial Systems, improved environmental impact and improved safety. This paper describes the fundamentals that a well designed and considered legal framework for a UTM system should address, in order to provide much needed certainty that can guide all stakeholders to a regulatory path that leads to safe maximized utility of drones in shared airspaceItem Open Access Modeling and characterization of traffic flow patterns and identification of airspace density for UTM application(IEEE, 2022-12-12) Alharbi, Abdulrahman; Petrunin, Ivan; Panagiotakopoulos, DimitriosCurrent airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas represe ting safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.Item Open Access Radar discrimination of small airborne targets through kinematic features and machine learning(IEEE, 2022-10-31) Doumard, Timothée; Gañán Riesco, Fabio; Petrunin, Ivan; Panagiotakopoulos, Dimitrios; Bennett, Cameron; Harman, StephenThis work studies binary classification problem for small airborne targets (drones vs other) by means of their trajectory analysis. For this purpose a set of the kinematic features extracted from drone trajectories using radar detections with a classification scheme that utilises Random Forests is proposed. The development is based on experimental data acquired from the Holographic radar from Aveillant Ltd. An approach for real-time classification is proposed, where an adaptive sliding window procedure is employed to make predictions over time from trajectories. Several models utilising different kinematic features (angle, slope, velocity, and their combination) are studied. The best model achieves an accuracy of more than 95%. In addition, fundamental issues with imbalanced datasets in the context of this topic are raised and illustrated using the collected data.Item Open Access Rule-based conflict management for unmanned traffic management scenarios(IEEE, 2020-11-18) Alharbi, Abdulrahman; Poujade, Arturo; Malandrakis, Konstantinos; Petrunin, Ivan; Panagiotakopoulos, Dimitrios; Tsourdos, AntoniosThe growing use of Unmanned Aerial Vehicles (UAVs) operations will require effective conflict management to keep the shared airspace safe and avoid conflicts among airspace users. Conflicts pose high risk and hazard to human lives and assets as they ma may result in financial and human loss. The proposed rule-based conflict management model consists of three main stages. The first stage includes strategic deconfliction during the flight plan generation. The second stage, pre-tactical deconfliction, applies a ground delay to the agent to resolve the conflict. The third stage corresponds to the tactical deconfliction, where the drone hovers or loiter in the last waypoint before the conflict area until the conflict time window passes. The proposed method differs from most existing conflict management approaches in that it applies deconfliction methods sequentially using a rule-based strategy. Furthermore, a high number of published studies do not consider realistic airspace constraints and potential airspace modernization concepts such as dynamic flight restrictions Assessment and validation are performed in three simulation scenarios that consider different patterns of the airspace availability in the areas where flights may be restricted, such as airfields, recreational areas, and prisons. The Particle Swarm Optimization (PSO) algorithm was used for drone path planning. For the simulated scenarios all of the conflicts were resolved after implementation of the proposed method. The implemented method is simple, flexible and suitable for the management of more complex and dense airspaces.Item Open Access Seamless handover in urban 5G-UAV systems using entropy weighted method(World Academy of Science, Engineering and Technology, 2021-12-14) Warrier, Anirudh; Al-Rubaye, Saba; Panagiotakopoulos, Dimitrios; Inalhan, Gokhan; Tsourdos, AntoniosThe demand for increased data transfer rate and network traffic capacity has given rise to the concept of heterogeneous networks. Heterogeneous networks are wireless networks, consisting of devices using different underlying radio access technologies (RAT). For Unmanned Aerial Vehicles (UAVs) this enhanced data rate and network capacity is even more critical especially in their applications of medicine, delivery missions and military. In an urban heterogeneous network environment, the UAVs must be able switch seamlessly from one base station(BS) to another for maintaining a reliable link. Therefore, seamless handover in such urban environments have become a major challenge. In this paper, a novel scheme to achieve seamless handover is developed, an algorithm based on Received Signal Strength (RSS) criterion for network selection is used and Entropy Weighted Method (EWM) is implemented for decision making. Seamless handover using EWM decision-making is demonstrated successfully for a UAV moving across fifth generation(5G) and long-term evolution (LTE) networks via a simulation level analysis. Thus, a solution for UAV-5G communication, specifically the mobility challenge in heterogeneous networks is solved and this work could act as step forward in making UAV-5G architecture integration a possibility.Item Open Access UAV path planning optimization based on GNSS quality and mission requirements(AIAA, 2021-01-04) Nanos, Nikolaos; Kagan Isik, Oguz; Verdeguer Moreno, Ricardo; Petrunin, Ivan; Panagiotakopoulos, Dimitrios; Tsourdos, AntoniosOne of the most crucial factors for the overall success of an Unmanned Aerial Vehicle (UAV) mission is navigation performance, which is severely affected in Global Navigation Satellite Systems (GNSS) challenging environments. A solution to this problem could come through path planning optimization. This paper investigates the impact that GNSS quality information included in the UAV path planning process would have on the overall UAV mission success rate (MSR) when flying through an urban canyon. Number of visible satellites and Horizontal Dilution of Precision (HDOP) in addition to mission-specific requirements are given as input to the Particle Swarm Optimization (PSO) algorithm to calculate the optimal path for two cases. One includes the GNSS observables, and the other does not. Optimal paths for three different altitudes are obtained. All paths are simulated by a GNSS signal simulator, including a comprehensive multipath model. GNSS data are collected by a hardware receiver for analysis of the UAV positioning error and GNSS availability. Mission failures cases are defined accordingly, and the overall mission success rate (MSR) of each scenario is assessed. By analyzing the findings, it is concluded that in 83% of cases, the path planning process that included GNSS information was able to increase the MSR. Also, the increase in MSR was bigger when flying at low altitude.