A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks

Abstract

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.

A Wasserstein generative adversarial network with gradient penalty for active sonar signal reverberation suppression

“…As a hot research direction in the field of machine learning, GAN (Ashraf et al, 2021) has become a popular model in the field of deep learning due to its advantages of generating high-quality samples, learning unlabeled data, supporting multi-modal data and innovation (Zhan et al, 2019) (Dong and Yang, 2018). Recently, Gans have evolved from image generation to reverberation data generation Hu et al (2023). Gans are also used to generate spatial impulse responses, with the aim of enhancing high-quality RIRs with existing real RIRs (Ratnarajah et al, 2023).…”