A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Xian-bin, Sun; Jia, Xinming; Zheng, Yi; Wang, Zhen

doi:10.1631/fitee.2000181

Front Inform Technol Electron Eng

2021

DOI: 10.1631/fitee.2000181

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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Sun Xian-bin

Xinming Jia

Yi Zheng

et al.

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“…Nonetheless, they struggle to exploit longer temporal context information. To overcome this shortcoming, recurrent neural networks (RNNs) combine information from previous temporal windows, enabling theoretically unlimited contextual information to be incorporated (Sun et al, 2021). To combine the advantages of CNNs and RNNs, the two architectures can be employed together in the form of a single network with convolutional layers followed by recurrent layers.…”

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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%

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

Chen²,

Bai³

et al. 2022

Front. Mar. Sci.

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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Cited by 1 publication

References 19 publications

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

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

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