TY - GEN
T1 - RF-NeuralNet
T2 - 7th International Conference on Automation, Control and Robots, ICACR 2023
AU - Misbah, Maham
AU - Dil, Mahnoor
AU - Khalid, Waqas
AU - Kaleem, Zeeshan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89%.
AB - Unmanned aerial vehicles (UAVs) have emerged as a revolutionary technology with diverse applications in fields such as crop monitoring, logistics, and traffic surveillance. Despite all these advantages, they also pose certain challenges such as privacy breaches, potential collision risks with airplanes, and terrorism activities. To mitigate these concerns, various techniques have been developed for UAV detection. In this paper, we propose a computationally efficient deep learning network RF-NeuralNet for UAV detection and mode identification using RF fingerprints. The proposed network involves a multiple-level skip connection to mitigate the gradient vanishing problem and multiple-level pooling layers for deep-level feature extraction. We evaluate the performance of the proposed RF-NeuralNet based on multiple UAV monitoring tasks (i.e., UAV identification, classification, and operational mode). Our proposed framework outperformed other state-of-the-art models and achieved an overall accuracy of 89%.
KW - Drones
KW - Multiclass classification
KW - Neural Net
KW - Radio frequency
UR - https://www.scopus.com/pages/publications/85179507234
U2 - 10.1109/ICACR59381.2023.10314637
DO - 10.1109/ICACR59381.2023.10314637
M3 - Conference contribution
AN - SCOPUS:85179507234
T3 - 2023 7th International Conference on Automation, Control and Robots, ICACR 2023
SP - 163
EP - 167
BT - 2023 7th International Conference on Automation, Control and Robots, ICACR 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2023 through 6 August 2023
ER -
