Source depth discrimination based on deep learning using frequency-wavenumber domain features

Ao Li; Junxiong Wang; Xiang Pan

doi:10.1117/12.3034119

13 June 2024 Source depth discrimination based on deep learning using frequency-wavenumber domain features

Ao Li, Junxiong Wang, Xiang Pan

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131806Y (2024) https://doi.org/10.1117/12.3034119
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Source depth discrimination using a horizontal array is a challenging task. This paper addresses the discrimination of source depths by exploiting modal excitation differences of surface and underwater targets in a shallow water environment. The modal domain beamforming technique is utilized to estimate the wavenumber spectrum. Then frequency-wavenumber domain features are extracted as inputs of a convolutional neural network (CNN). The CNN is acted as a binary classifier for target depth estimation. The simulation experimental results have shown that the proposed method outperforms the conventional matched field processing.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ao Li, Junxiong Wang, and Xiang Pan "Source depth discrimination based on deep learning using frequency-wavenumber domain features", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131806Y (13 June 2024); https://doi.org/10.1117/12.3034119

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