Drone-aided ubiquitous applications play important roles in our daily lives. Accurate recognition of drones is required in aviation management due to their potential risks and disasters. Radiofrequency (RF) fingerprinting-based recognition technology based on deep learning (DL) is considered an effective approach to extracting hidden abstract features from the RF data of drones. Existing deep learning-based methods are either high computational burdens or have low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has a high recognition accuracy, fast running time, and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracies of 99.5% and 74.1%, respectively, on four and eight classes of RF drone datasets.


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    Title :

    Deep Complex-Valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting


    Contributors:
    Jie Yang (author) / Hao Gu (author) / Chenhan Hu (author) / Xixi Zhang (author) / Guan Gui (author) / Haris Gacanin (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





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