Small Size Array Underwater Acoustic DOA Estimation Based on Direction-Dependent Transmission Response

Li, Jiaheng; Tong, Feng; Zhou, Yuehai; Yi, Yang; Hu, Z.

doi:10.1109/tvt.2022.3197922

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…The CNN is used to efficiently extract high-level information, while the RNN is used to ensure that temporal context information is efficiently modeled to enable DOA estimation. To adopt challenging conditions like multipath propagation of highfrequency sound signals in a shallow water environment, the proposed method is validated on a synthetic dataset generated by BELLHOP Jing et al (2018); Han et al (2021); Li et al (2022). Similarly, to validate the performance of the proposed methodology in actual complex underwater environmental effects we test it on real data obtained through experiments in the sea.…”

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confidence: 99%

Deep learning-based DOA estimation using CRNN for underwater acoustic arrays

Chen²,

Bai³

et al. 2022

Front. Mar. Sci.

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In the marine environment, estimating the direction of arrival (DOA) is challenging because of the multipath signals and low signal-to-noise ratio (SNR). In this paper, we propose a convolutional recurrent neural network (CRNN)-based method for underwater DOA estimation using an acoustic array. The proposed CRNN takes the phase component of the short-time Fourier transform of the array signals as the input feature. The convolutional part of the CRNN extracts high-level features, while the recurrent component captures the temporal dependencies of the features. Moreover, we introduce a residual connection to further improve the performance of DOA estimation. We train the CRNN with multipath signals generated by the BELLHOP model and a uniform line array. Experimental results show that the proposed CRNN yields high-accuracy DOA estimation at different SNR levels, significantly outperforming existing methods. The proposed CRNN also exhibits a relatively short processing time for DOA estimation, extending its applicability.

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confidence: 99%