Small sample underwater acoustic signal modulation classification and recognition based on ensemble learning

Cong Liu; Dong Han; Xinyang Zhang; Ning Li

doi:10.1117/12.2674274

28 April 2023 Small sample underwater acoustic signal modulation classification and recognition based on ensemble learning

Cong Liu, Dong Han, Xinyang Zhang, Ning Li

Author Affiliations +

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262604 (2023) https://doi.org/10.1117/12.2674274
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Automatic classification and recognition of underwater acoustic communication signal modulation plays a key role in the field of underwater communication and confrontation, and it is a necessary combat capability in modern naval warfare. However, the recognition method based on Gaussian white noise environment established on the basis of traditional theory is still difficult to recognize in the background of underwater impulse noise. Modulation identification faces enormous challenges. In response to this problem, this study proposes an ensemble learning classification algorithm based on the fusion of convolutional neural network feature extraction and artificial feature extraction. Under small sample conditions, the simulation results show that the recognition rate of the ensemble learning method after feature fusion in the mixed Signal samples with multiple signal-to-noise ratios is improved to 99.7%, and it is robust to underwater impulse noise interference.

Citation Download Citation

Cong Liu, Dong Han, Xinyang Zhang, and Ning Li "Small sample underwater acoustic signal modulation classification and recognition based on ensemble learning", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262604 (28 April 2023); https://doi.org/10.1117/12.2674274

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