Acoustic signal recognition of UAV based on GAF and convolutional neural network

Tengda Liu; Yaochuang Cai; Ruixia Cheng; Yiwen Zhang

doi:10.1117/12.2643460

9 October 2022 Acoustic signal recognition of UAV based on GAF and convolutional neural network

Tengda Liu, Yaochuang Cai, Ruixia Cheng, Yiwen Zhang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 1224609 (2022) https://doi.org/10.1117/12.2643460
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

Aiming at the problem that the traditional classification and recognition scheme has few effective features and low recognition accuracy in the process of UAV acoustic signal recognition, a new method based on Gramian Angular Field (GAF) and Convolutional Neural Network (CNN) is proposed. Using the methods of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), one-dimensional UAV acoustic signals are mapped into two-dimensional images. Then, a convolutional neural network model suitable for UAV acoustic signal classification is designed, and the two-dimensional image is used as input, thereby realizing UAV acoustic signal recognition. The experimental results show that compared with the GASF algorithm, the GADF algorithm has higher recognition accuracy and better reliability. The GAF algorithm has more significant features of the UAV acoustic signal than the traditional feature extraction algorithm, and is more suitable for using convolutional neural network for recognition. Compared with other UAV acoustic signal recognition schemes, the GAF combined with CNN method designed in this paper has better performance.

Citation Download Citation

Tengda Liu, Yaochuang Cai, Ruixia Cheng, and Yiwen Zhang "Acoustic signal recognition of UAV based on GAF and convolutional neural network", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 1224609 (9 October 2022); https://doi.org/10.1117/12.2643460

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