A recurrent plot based stochastic nonlinear ray propagation model for underwater signal propagation

Yao, Hongxun; Wang, Haiyan; Xu, Yong; Kurths, Jüergen

doi:10.1088/1367-2630/ab8caf

Cited by 3 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, BSS is widely used and performs well with the assumption of a linear mixing procedure [17][18][19][20][21], in which nonlinear components are neglected. However, in fact, the nonlinear effect is non-negligible in underwater acoustic channels, such as nonlinear distortion caused by hydrodynamics and the adiabatic relation between pressure and density [22], nonlinearity in devices like power amplifiers [23][24][25][26], the nonlinear interaction of collimated plane waves [27], the thermal current and nonlinear fluids such as relaxing fluids, bubbly liquids and fluids in saturated porous solids [28][29][30][31]. Linear methods cannot separate the signals with nonlinear components to a high degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

show abstract

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

CA_MobileNetV2 for Underwater Acoustic Target Recognition

Tian,

Haiyang,

Haiyan

et al. 2023

2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

View full text Add to dashboard Cite

Angular Spectrum of Acoustic Pulses at Long Ranges

Макаров

Kon’kov

2022

JMSE

View full text Add to dashboard Cite

Long-range propagation of sound pulses in the deep ocean is considered. A new method for the estimation of the pulse angular spectrum is presented. The method is based on the Husimi transform of a wave field and can be realized with a short vertical array of nondirectional hydrophones. As a result, one obtains a diagram of the arrival pattern in the time–angle plane. The method is applied to a model of the underwater sound channel in the Sea of Japan. Special attention is paid to sound scattering on a cold synoptic eddy along the waveguide. It is shown that the synoptic eddy leads to a splitting of the individual ray’s arrivals into clusters with close angles and times. The random sound-speed perturbation induced by internal waves blurs these clusters into a fuzzy background and simultaneously broaden the angular spectrum of pulses. Nevertheless, it is found that the latter effect is relatively weak for short vertical arrays. In particular, it is shown that increasing the array length from 10 to 30 m results in the separation of the arrivals with opposite angles.

show abstract

A recurrent plot based stochastic nonlinear ray propagation model for underwater signal propagation

Cited by 3 publications

References 41 publications

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

CA_MobileNetV2 for Underwater Acoustic Target Recognition

Angular Spectrum of Acoustic Pulses at Long Ranges

Contact Info

Product

Resources

About