A Coupled Mode Method for Low Frequency Distant Reverberation in Deep Water Environment with Ice Covers

Zuo, Yiru; Gao, Bo; Song, Wenhua; Pang, Jie; Mo, Dongpeng

doi:10.1109/coa50123.2021.9520003

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…In this paper, only seabed reverberation and clutter are considered. In order to improve the computational efficiency of the model, the ray-mode approximate correspondence [5] (see table 1) is used to only consider acoustic rays in contact with the seabed. For the seabed scattering process, this paper considers the bistatic elliptic ring.…”

Section: Basic Theorymentioning

confidence: 99%

Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea

Zou

Gao

2023

J. Phys.: Conf. Ser.

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Reverberation clutter induced by discrete strong scatterers is an important reason which will increase the false alarm probability of active detection, which seriously affects the performance of active sonar detection. In this paper, taking seamount as an example, the theoretical modeling and simulation of the clutter in the deep-sea induced by seamount are carried out by using the ray-mode theory. Through ray-mode approximation, the acoustic rays in surface and reverse channels are ignored, a theoretical model is proposed to calculate the seabed reverberation clutter induced by seamounts, and the physical mechanism of how the seabed reverberation clutter is induced by seamounts is analyzed. The results show that reverberation intensity will increase in the location of seamount and induce discrete reverberation clutter. The greater the fluctuation of seamount, the greater the reverberation clutter intensity.

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Section: Basic Theorymentioning

confidence: 99%

Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea

Zou

Gao

2023

J. Phys.: Conf. Ser.

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A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks

Rao

Zhao

et al. 2023

Applied Sciences

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Reverberation characteristics must be considered in the design of sonar. The research on reverberation characteristics is based on a large number of actual reverberation data. Due to the cost of trials, it is not easy to obtain actual lake and sea trial reverberation data, which leads to a lack of actual reverberation data. Traditionally, reverberation data are obtained by modeling the generation mechanism of seafloor reverberation. The usability of the models requires a large amount of actual seafloor reverberation data to verify. In terms of the reverberation modeling theory, scattering models are mostly empirical, computationally intensive and inefficient. In order to solve the above obstacles, we propose a shallow seafloor reverberation data simulation method based on the generative adversarial network (GAN), which uses a small amount of actual reverberation data as reference samples to train the GAN to generate more reverberation data. The reverberation data generated by the GAN are compared with that simulated by traditional methods, and it is found that the reverberation data generated by the GAN meet the reverberation characteristics. Once the network is trained, the reverberation data are generated with very little computation. In addition, the method is universal and can be applied to any sea area. Compared with the traditional method, this method has a simple modeling idea, less computation and strong universality. It can be used as an alternative method for sea trials to provide data support for the study of seafloor reverberation characteristics, and it has broad application prospects in antireverberation technology research and active sonar design.

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A Coupled Mode Method for Low Frequency Distant Reverberation in Deep Water Environment with Ice Covers

Cited by 2 publications

References 7 publications

Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea

Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea

A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks

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