Browsing by Author "Fraser, Benjamin"
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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 Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection(IEEE, 2021-11-15) Fraser, Benjamin; Al-Rubaye, Saba; Aslam, Sohaib; Tsourdos, AntoniosIn this paper the general susceptibilities of Unmanned Aerial Vehicles (UAVs) against modern-cyber threats are explored and potential solutions proposed. This is achieved by applying digital-twin architectures and data-driven methods to UAVs to facilitate identification of real-time intrusions and anomalies. These concepts are validated by performing novelty detection on open access UAV flight data with GPS spoofing attacks, which represents a typical system use-case. Multiple machine learning models are trained to demonstrate the feasibility of detecting modern cyber-intrusions and anomalies using the digital-twin architecture. This includes both classical and deep learning techniques to help identify the most suitable model types for the proposed design. The overall results are positive and help highlight the potential of digital-twin architectures for the UAV contexts.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 Reducing viral transmission through AI-based crowd monitoring and social distancing analysis(IEEE, 2022-10-13) Fraser, Benjamin; Copp, Brendan; Singh, Gurpreet; Keyvan, Orhan; Bian, Tongfei; Sonntag, Valentin; Xing, Yang; Guo, Weisi; Tsourdos, AntoniosThis paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.Item Open Access Uncovering drone intentions using control physics informed machine learning(Springer Nature, 2024-02-24) Perrusquía, Adolfo; Guo, Weisi; Fraser, Benjamin; Wei, Zhuangkun; This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/V026763/1 and by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme.Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.Item Open Access Uncovering Drone Intentions using Control Physics Informed Machine Learning: data(Cranfield University, 2024-03-04 10:36) Perrusquia Guzman, Adolfo; Wei, Zhuangkun; Guo, Weisi; Fraser, BenjaminThis repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning"