Design of UAV signal classification system based on neural network encoder

Bin Wu; Taoyu Huangfu; Qi Dai; Wanqing Zhang

doi:10.1117/12.3033624

13 June 2024 Design of UAV signal classification system based on neural network encoder

Bin Wu, Taoyu Huangfu, Qi Dai, Wanqing Zhang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801S (2024) https://doi.org/10.1117/12.3033624
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Unmanned Aerial vehicles (UAVs) are increasingly used in healthcare, agriculture, environment, disaster management and other fields. Therefore, it is very important to research and build a system that can accurately identify UAV signals in a complex environment. This paper aims to explore a UAV signal recognition system based on the classification of the signals sent by the UAV and its controller. Wavelet transform is used to extract features, and neural network encoder is used to classify UAV signals and controller signals to realize the distinction between UAV signals and controller signals, different UAV models and different controller models. This model adopts the architecture of an autoencoder and introduces innovative design in two frameworks. The initial framework incorporates skip connections within the residual layers, ensuring the preservation of original information during convolutional operations. Meanwhile, the second framework capitalizes on this preserved information to extract supplementary features. These designs collectively enable the model to achieve a more profound understanding of the feature information embedded within the data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Wu, Taoyu Huangfu, Qi Dai, and Wanqing Zhang "Design of UAV signal classification system based on neural network encoder", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801S (13 June 2024); https://doi.org/10.1117/12.3033624

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