Quantum CNN for Detection and Identification of UAV-Enabled Non-Terrestrial Networks

Unmanned aerial vehicles (UAVs) have become essential elements of non-terrestrial networks (NTNs) utilized in various sectors, including agriculture, public safety, surveillance, and critical military operations. However, in addition to the benefits of these NTNs, they are increasingly being exploited for malicious purposes, leading to a heightened need for timely detection and identification. Despite advances in UAV detection, challenges remain, particularly in dealing with different UAV types, the payloads they carry, and their flight characteristics. Relying on a single convolutional neural network (CNN) for UAV detection and identification presents difficulties in managing diverse datasets and capturing complex, interdependent relationships. To address this, we propose a novel approach that integrates the visual geometry group-based CNN for UAV detection and the mask region-based CNN for the identification of various traits of UAVs. Additionally, to overcome the computational complexity of deep convolutional layers, we introduce a quantum computation-based CNN (QCNN) instead of the conventional CNN, applied in both the visual geometry group-based detection and mask region-based identification processes, jointly termed VM-QCNN. To effectively deploy VM-QCNN, we enhance the dataset by applying data augmentation techniques, which add diversity to the training data. This ensures the model accurately detects various UAV types, payload categories, and flight characteristics. Performance evaluation through simulations demonstrates that the VM-QCNN approach significantly improves the detection of malicious UAVs compared to competitive algorithms.