[4] SHI X F, YANG C Q, XIE W G, et al. Anti-drone system with multiple surveillance technologies: architecture, im-plementation, and challenges[J]. IEEE Communications Magazine, 2018, 56(4): 68-74.
[6] Dedrone. Worldwide drone incidents[EB/OL]. (2024-01-06)[2024-03-01]. https: //www. dedrone. com/resources/incidents-new/all.
[7] ZHAO C D, CHEN C Y, HE Z P, et al. Application of auxiliary classifier Wasserstein generative adversarial networks in wireless signal classification of illegal unmanned aerial vehicles[J]. Applied Sciences, 2018, 8(12): 2664.
[9] AL-SA'D M F, AL-ALI A, MOHAMED A, et al. RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database[J]. Future Generation Computer Systems, 2019, 100: 86-97.
[10] ALLAHHAM M S, KHATTAB T, MOHAMED A. Deep learning for RF-based drone detection and identification: a multi-channel 1-D convolutional neural networks approach[C]//2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). Doha: IEEE, 2020: 112-117.
[11] EZUMA M, ERDEN F, KUMAR ANJINAPPA C, et al. Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and bluetooth interference[J]. IEEE Open Journal of the Communications Society, 2019, 1: 60-76.
[12] BASAK S, RAJENDRAN S, POLLIN S, et al. Drone classification from RF fingerprints using deep residual nets[C]//2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). Bangalore: IEEE, 2021: 548-555.
[14] KHAN M A, MENOUAR H, ELDEEB A, et al. On the detection of unauthorized drones-techniques and future perspectives: a review[J]. IEEE Sensors Journal, 2022, 22(12): 11439-11455.
[15] BASAK S, RAJENDRAN S, POLLIN S, et al. Combined RF-based drone detection and classification[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 8(1): 111-120.
[16] OUYANG D L, HE S, ZHANG G Z, et al. Efficient multi-scale attention module with cross-spatial learning[C]//2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island: IEEE, 2023: 1-5.
[17] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 1800-1807.
[18] CHEN J R, KAO S H, HE H, et al. Run, don't walk: chasing higher FLOPS for faster neural networks[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023:12021-12031.
[19] SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City: IEEE, 2018: 4510-4520.
[20] MEDAIYESE O O, EZUMA M, LAUF A P, et al. Semi-supervised learning framework for UAV detection[C]//2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). Helsinki: IEEE, 2021: 1185-1190.
[21] OZTURK E, ERDEN F, GUVENC I. RF-based low-SNR classification of UAVs using convolutional neural networks[R]. Los Alamos: arXiv: Preprint, 2020: arXiv: 2009. 05519.
[22] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Computer Vision-ECCV 2016. Cham: Springer, 2016: 21-37.
[23] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 10778-10787.