TFF-CNN:  time-frequency fusion-based CNN for DOA estimation of underwater acoustic array signal

Tong Zhang; Jun Liu

doi:10.1117/12.2689869

10 August 2023 TFF-CNN: time-frequency fusion-based CNN for DOA estimation of underwater acoustic array signal

Tong Zhang, Jun Liu

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274804 (2023) https://doi.org/10.1117/12.2689869
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In the Marine environment, underwater acoustic positioning signals are susceptible to complex underwater acoustic channels and multiple types of underwater noise, resulting in inaccurate DOA estimation, which will significantly reduce the accuracy and reliability of underwater positioning, and the real-time performance of underwater positioning cannot be guaranteed. In this paper, a Time-Frequency fusion based convolutional neural network (TFF-CNN) method for underwater DOA estimation is proposed. In this method, fusion feature maps are constructed based on Time-Frequency characteristics of signals, and a novel convolutional neural network with the deconvolution layer is designed to enhance feature representation and noise resistance. The experimental results show that compared with traditional methods and other networks, the proposed algorithm has great advantages in the estimation accuracy and speed, especially under non-Gaussian noise.

Citation Download Citation

Tong Zhang and Jun Liu "TFF-CNN: time-frequency fusion-based CNN for DOA estimation of underwater acoustic array signal", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274804 (10 August 2023); https://doi.org/10.1117/12.2689869

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