Fraser, BenjaminPerrusquía, AdolfoPanagiotakopoulos, DimitriosGuo, Weisi2023-09-292023-09-292023-09-22Fraser B, Perrusquía A, Panagiotakopoulous D, Guo W. (2023) Hybrid deep neural networks for drone high level intent classification using non-cooperative radar data. In: 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 19-21 July 2023, Santa Cruz de Tenerife, Spain979-8-3503-2298-9https://doi.org/10.1109/ICECCME57830.2023.10252859https://dspace.lib.cranfield.ac.uk/handle/1826/20308The 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.enAttribution-NonCommercial 4.0 Internationaldroneshigh-level intentclassificationnovelty detectiondeep neural networksConference paper979-8-3503-2297-2